Socrates-Qwen-14B: Multi-Context Analysis

• Socrates-Qwen-14B is a designation for a range of Qwen-14B-based models that employ Socratic methodologies in simulation, interactive learning, and evaluation contexts.
• The fine-tuned model for social science experiments achieves notable improvements in distributional accuracy and demographic parity reduction compared to its base models.
• It also serves as a conceptual backbone for pedagogical strategies, mediation benchmarks, and multimodal reasoning frameworks across distinct research efforts.

Socrates-Qwen-14B is a designation used in multiple recent research contexts for Qwen-14B-centered systems or proposed instantiations associated with Socratic, interactive, or reflective workflows. One paper defines it explicitly as a fine-tuned Qwen2.5-14B-Instruct model for simulating the outcomes of social science experiments by predicting distributions of human responses under experimental conditions (Kolluri et al., 6 Sep 2025). Other works use the term prospectively for student-led question-asking, mediator evaluation, multimodal self-questioning, or critique-model adaptation, while several systems named “Socrates” or “SoCRATES” explicitly do not introduce, use, or release a model called Socrates-Qwen-14B (Ambati et al., 15 Dec 2025, Yun et al., 4 Jun 2026, Hu et al., 6 Jan 2025, Yang et al., 8 Aug 2025, Gu et al., 2023, Barnaby et al., 9 Apr 2026, Shao et al., 27 Nov 2025). This suggests that the designation is not a single canonical checkpoint, but a family of paper-specific constructions anchored to Qwen-14B backbones and Socratic interaction patterns.

1. Terminological scope and literature map

Across the cited literature, “Socrates-Qwen-14B” does not denote a uniform artifact. In some cases it is an explicit trained model; in others it is a hypothetical or implementation-oriented extension of a Socrates-branded method to a Qwen-14B backbone; and in several similarly named systems the authors state directly that no such Qwen-14B model is present (Kolluri et al., 6 Sep 2025, Yang et al., 8 Aug 2025, Yun et al., 4 Jun 2026, Gu et al., 2023, Barnaby et al., 9 Apr 2026, Shao et al., 27 Nov 2025).

Work	Relation to “Socrates-Qwen-14B”	Qwen-14B status
“Finetuning LLMs for Human Behavior Prediction in Social Science Experiments” (Kolluri et al., 6 Sep 2025)	Explicit model name	Fine-tuned Qwen2.5-14B-Instruct; data, models, and code released
“Qwen Technical Report” (Bai et al., 2023)	Backbone context	14B base/chat/code/math variants documented
“Socratic Students” (Ambati et al., 15 Dec 2025)	Prospective instantiation	Pipeline applies to Qwen2.5-14B-Instruct, but 14B student results are not reported
“Learning by Teaching” (Yang et al., 8 Aug 2025)	Related Socrates pedagogy	Paper states it does not mention or introduce a deployment named “Socrates-Qwen-14B”
“SoCRATES” mediation benchmark (Yun et al., 4 Jun 2026)	Evaluation context	Qwen-14B is not benchmarked
“Digital Socrates” (Gu et al., 2023)	Related critique model	No official Qwen-14B release
“Choose, Don’t Label” (Barnaby et al., 9 Apr 2026)	Homonymous Socrates tool	Qwen-14B is not mentioned
“Asking like Socrates” (Shao et al., 27 Nov 2025)	Related multimodal methodology	Paper reports a Qwen2.5-VL-7B model, not a 14B checkpoint

This dispersion matters because the same label can refer to substantially different objectives: distributional human-behavior prediction, interactive student learning, mediation, multimodal evidence-seeking, or explanation critique. For technical reading, the term is therefore best interpreted paper-locally rather than as a stable benchmarked model family.

2. Distribution-first social-science simulation model

The most concrete and fully specified use appears in “Finetuning LLMs for Human Behavior Prediction in Social Science Experiments,” where Socrates-Qwen-14B is defined as a fine-tuned LLM for simulating social science experiments by predicting the distribution of human responses to stimulus–question pairs under experimental conditions, optionally conditioned on participant demographics (Kolluri et al., 6 Sep 2025).

Its base model is Qwen2.5-14B-Instruct. The training corpus, SocSci210, comprises 2.9 million individual responses from 400,491 participants across 210 open-source social science experiments reconstructed from the NSF Time-sharing Experiments for the Social Sciences repository. The corpus spans 1,197 outcomes and 1,194 conditions, yielding 5,998 unique stimuli, and includes rich demographic attributes such as age, gender, education, employment, marital status, income, housing, ideology, party identification, and ethnicity. The evaluation task is deliberately distributional rather than purely pointwise: for each condition–outcome pair, model-generated responses are compared with empirical human response distributions using first-order Wasserstein distance after standardizing ordinal or binary outcomes to $[0,1]$.

The paper also defines an individual-response accuracy metric,

$$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$

and evaluates generalization in four regimes: completely unseen studies, unseen conditions within seen studies, unseen outcomes within seen studies, and unseen participants under limited pilot-data finetuning. The unseen-studies split uses 170 training studies and 40 held-out test studies. Within-study condition and outcome splits use a 75\%/25\% partition, and participant-level experiments assess learning curves from pilot subsets at 1, 5, 10, 20, 30, 40, and 50\% of available participants.

Training uses full fine-tuning rather than adapters: 1 epoch, global batch size 256, learning rate $1\times10^{-5}$ for supervised fine-tuning and $1\times10^{-6}$ for DPO, cosine schedule, warm-up ratio 0.05, and weight decay 0.1. The reported hardware is 8× NVIDIA A100 80GB GPUs, with runs lasting 4–24 hours. Inference uses temperature 0.6, top_p 0.9, and max_length 4096. Prompt templates enumerate persona demographics in bullet form, then provide the condition stimulus and the outcome question with explicit output-format instructions such as “Only return an integer from 1 to 6, nothing else.”

On completely unseen studies, Socrates-Qwen-14B achieves Wasserstein distance 0.151, compared with 0.205 for its base Qwen2.5-14B and 0.174 for GPT-4o. The paper reports this as a 26.3\% improvement relative to the base model and a 13.2\% advantage over GPT-4o. Its supervised-fine-tuned variant attains 69.5\% individual accuracy, whereas a DPO-optimized Qwen2.5-14B variant reaches 74.0\% individual accuracy. The authors also report that finetuning reduces demographic parity by approximately 10.6\% while improving average subgroup distributional alignment. Data, trained models, and finetuning code are released at stanfordhci.github.io/socrates.

3. Qwen-14B as the technical substrate

The architectural and training substrate for many prospective “Socrates-Qwen-14B” constructions is the 14B branch of the Qwen family documented in the “Qwen Technical Report” (Bai et al., 2023). That report defines base pretrained models and aligned chat models at 1.8B, 7B, and 14B, with 14B variants also available for Code-Qwen, Code-Qwen-Chat, and Math-Qwen-Chat. In that family, the 14B base checkpoint is positioned as the flagship developer-scale model, and the aligned chat branch includes both Qwen-14B-Chat and a further RLHF-tuned “Qwen-14B-Chat RLHF.”

Qwen-14B is a decoder-only Transformer in the LLaMA-style lineage with 40 layers, 40 attention heads, hidden size 5120, and an FFN dimension reduced from $4\times\text{hidden}$ to $\frac{8}{3}\times\text{hidden}$ using a GLU-style activation. The model uses BPE tokenization via tiktoken, initialized from cl100k, with a final vocabulary of approximately 152K tokens and single-digit number splitting. Positional encoding uses RoPE with the inverse frequency matrix retained in FP32, attention is standard multi-head attention with QKV biases, normalization is pre-norm RMSNorm, activation is SwiGLU, and embeddings are untied. Stability and efficiency mechanisms include FlashAttention and BF16 mixed precision.

The reported pretraining objective is autoregressive next-token prediction,

$L = -\sum_t \log p_\theta(x_t \mid x_{<t}),$

over up to 3 trillion multilingual tokens from public web documents, encyclopedias, books, code, and related sources. Training uses AdamW with $\beta_1=0.9$, $\beta_2=0.95$, $\epsilon=1\mathrm{e}{-8}$, peak learning rate $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$0, cosine decay to 10\% of peak, and global batch size 4M tokens. Although trained at context length 2048, Qwen-14B is extended at inference to at least 8192 tokens through NTK-aware interpolation, dynamic NTK-aware interpolation, LogN-Scaling, and layer-wise window attention. For Qwen-14B, perplexity at 2048/4096/8192/16384 tokens improves from 3.46/22.79/334.65/3168.35 to 3.46/3.29/3.18/3.42 when dynamic NTK, LogN, and window attention are enabled.

Alignment for Qwen-Chat combines supervised fine-tuning and RLHF. The SFT stage uses ChatML-style formatting with role tokens such as <|im_start|> and <|im_end|>, sequence length 2048, batch size 128, total 4000 steps, peak learning rate $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$1, weight decay 0.1, dropout 0.1, and gradient clipping at 1.0. The RLHF stage employs reward modeling and PPO with KL regularization,

$$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$2

using $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$3, policy learning rate $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$4, and value learning rate $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$5.

In benchmarks, Qwen-14B base reports MMLU 66.3 (5-shot), C-Eval 72.1 (5-shot), GSM8K 61.3 (8-shot), MATH 24.8 (4-shot), HumanEval 40.8 (0-shot), MBPP 53.4 (0-shot), and BBH 53.4 (3-shot). Qwen-14B-Chat reports MMLU 64.6 (0-shot) / 66.5 (5-shot), C-Eval 69.8 / 71.7, GSM8K 60.1 / 59.3, HumanEval 43.9, and BBH 46.9 / 58.7. Code-Qwen-14B-Chat reaches HumanEval pass@1 of 66.4, and Math-Qwen-Chat-14B reaches GSM8K 69.8, MATH 24.2, Math401 85.0, and Math23K 78.4. These technical properties explain why later works repeatedly treat Qwen-14B as a plausible backbone for Socratic, self-questioning, or tool-mediated systems, even when no standardized “Socrates-Qwen-14B” release exists.

4. Student-centered interaction and pedagogical lineages

A second major lineage associates the label with interactive learning paradigms in which the model is not primarily a simulator of human responses, but either a student that learns by asking questions or a partner that must be taught by students. These lines are conceptually related yet operationally distinct (Ambati et al., 15 Dec 2025, Yang et al., 8 Aug 2025).

In “Socratic Students,” Socrates-Qwen-14B is the Qwen2.5-14B-Instruct instantiation of a student-led protocol in which the model improves math and coding performance by recognizing uncertainty, asking targeted questions to a stronger teacher, and retaining the answers in-context. The interaction budget is fixed at 11 turns, with a teacher greeting at turn 0, student questions on odd turns, and teacher answers on even turns. A CoT-guided variant instructs the student to summarize known information, list missing subgoals, and ask one precise question. Optional pre-assessment or mid-assessment steps insert structured feedback, with pre-assessment helping math most and mid-assessment helping coding most. Question quality is improved through DPO over preference pairs $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$6, where the preferred question maximizes downstream Pass@k after teacher response. Training uses QLoRA with rank $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$7, $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$8, dropout 0.05, AdamW with $$\mathrm{Acc} = 1 - \frac{1}{N}\sum_{(P,c,o)} \frac{|F'(P,c,o)-r|}{r_{\max}-r_{\min}},$$9, $1\times10^{-5}$0, learning rate $1\times10^{-5}$1, cosine decay, 5 epochs, and early stopping on NVIDIA RTX A6000 GPUs. The paper’s detailed curves are reported for Qwen-7B and Mistral-7B rather than Qwen-14B, but it states that the same pipeline applies verbatim to Qwen2.5-14B-Instruct (Ambati et al., 15 Dec 2025).

A different pedagogical inversion appears in “Learning by Teaching,” whose Socrates system deliberately shifts away from the “LLM-as-tutor” model by making students the instructors of an LLM. The system engineers knowledge gaps so that the model cannot solve problems from prior knowledge alone; students must bridge those gaps with explicit, stepwise prompts. The paper emphasizes two gap-construction strategies: non-existing scenarios, such as arbitrarily remapped number systems or hypothetical assembly instructions like SWAPADD R1, R2, R3, and guided mathematical reasoning, such as forcing explicit Boolean-algebra transformations into canonical sum-of-minterms. Student prompt strategies include Chain-of-Thought, few-shot prompting, and self-consistency, while the implementation uses JSON assignment files, a Jupyter/Voilà playground, and a grader that re-queries the designated LLM and checks outputs with an LLM-based Yes/No comparator. In classroom deployment, however, the paper states explicitly that it does not mention or introduce a deployment named “Socrates-Qwen-14B.” The models actually used were OpenAI’s gpt-3.5-turbo and gpt-4o, and Google’s gemini-1.0-pro, all accessed via API. In a quasi-experimental undergraduate CS course, assignments improved with $1\times10^{-5}$2, projects with $1\times10^{-5}$3, and exams showed a non-significant increase with $1\times10^{-5}$4. The most expensive grading model, gpt-4o, consumed \$169.90, and the framework was released as open-source code at github.com/junli-cuny/Socrates (Yang et al., 8 Aug 2025).

These two strands share a Socratic emphasis on explicit gap identification, iterative prompting, and stepwise reasoning, but they should not be conflated. One treats Qwen-14B as the student itself; the other uses the name “Socrates” for a pedagogical system that, in the reported deployment, does not use Qwen-14B at all.

5. Benchmarking and evaluator contexts

The label also appears in benchmark and evaluator settings where Qwen-14B is a target or proposed backbone rather than a reported result. The clearest case is “SoCRATES,” a benchmark for proactive LLM mediation across realistic multi-domain disputes (Yun et al., 4 Jun 2026).

SoCRATES constructs 40 hard mediation scenarios across eight domains—transactional, healthcare, environmental, business-to-business, public-policy, international, legal, and intra-organizational—and perturbs each along five socio-cognitive axes: strategic posture, party composition, history length, emotional reactivity, and cultural identity. Each base scenario expands into 15 conditions, producing 600 runs per mediator and 4,800 total. Agreement is scored by a topic-localized evaluator that only rates turns where a specific topic is actually advanced, then carries topic scores forward to form a consensus snapshot

$1\times10^{-5}$5

The benchmark further defines Intervention Timeliness, Intervention Effectiveness, and Consensus Gain. Validated against two expert annotators on 1,844 snippets from 144 mediator trajectories, the evaluator reaches Pearson $1\times10^{-5}$6 at the trajectory level and $1\times10^{-5}$7 at the outcome level, with expert inter-annotator agreement of Krippendorff’s $1\times10^{-5}$8.

Qwen-14B itself is not included in the reported experiments. The closest Qwen models are Qwen3-30B-Instruct and Qwen3-235B-Instruct. Across domains, Qwen3-235B achieves average consensus gain 30.7 and Qwen3-30B achieves 15.7, while GPT-5.4-mini tops the table at 34.4. Qwen3-235B averages approximately 76.4 on Intervention Timeliness and 24.6 on Intervention Effectiveness; Qwen3-30B is faster to intervene, at approximately 84.6 timeliness, but less helpful, at approximately 19.7 effectiveness. The paper therefore treats Qwen-14B as a plausible future mediator to be plugged into the same loop, using the provided when-to-intervene and how-to-intervene prompts, but does not supply absolute Qwen-14B scores (Yun et al., 4 Jun 2026).

A comparable pattern appears in “Digital Socrates,” which introduces explanation critiquing and releases DS-7B and DS-13B, both based on Llama 2 chat backbones rather than Qwen (Gu et al., 2023). The model takes a question, predicted answer, and explanation, then outputs a five-part critique

$1\times10^{-5}$9

where fdim is one of eight flaw dimensions and Esc is a 0–5 explanation-quality score. The accompanying DS Critique Bank contains 26,478 critiques over 4,091 questions. On the dev subset, DS-13B achieves flaw-dimension overlap 0.84 and score agreement within 1 point of crowd labels at 0.87, with Pearson $1\times10^{-6}$0. The paper states that no official Qwen-14B-based Digital Socrates is released, but that the method is backbone-agnostic and can be instantiated on Qwen-14B using the same prompt schema and training curriculum (Gu et al., 2023).

In both mediation and critique evaluation, therefore, “Socrates-Qwen-14B” functions less as a released benchmark entry than as a proposed instantiation point for well-specified evaluation protocols.

6. Homonymous systems, multimodal extensions, and non-equivalence

Several additional works use the name “Socrates” or “Socratic” in ways that are adjacent to, but not identical with, the Qwen-14B-centered constructions above. These works are important chiefly because they delimit what Socrates-Qwen-14B is not (Barnaby et al., 9 Apr 2026, Shao et al., 27 Nov 2025, Hu et al., 6 Jan 2025).

In “Choose, Don’t Label,” Socrates is an active-learning tool for program disambiguation based on multiple-choice queries whose options correspond to Hoare triples. The system optimizes query informativeness and interpretability over a finite hypothesis space and proves correctness of its active-learning loop under standard assumptions. The only named LLM is gpt-4o, used solely to translate synthesized logical queries into natural language. The paper states directly that Qwen-14B is not mentioned anywhere, so no Socrates-Qwen-14B artifact exists in that work (Barnaby et al., 9 Apr 2026).

In remote sensing, “Asking like Socrates” introduces RS-EoT and SocraticAgent, a language-driven evidence-seeking paradigm implemented on Qwen2.5-VL-7B-Instruct rather than a 14B model. The reported checkpoint is RS-EoT-7B, trained by SocraticAgent-generated reasoning traces and a two-stage GRPO schedule. The paper reports state-of-the-art performance on multiple remote-sensing VQA and grounding benchmarks, but explicitly notes that no checkpoint named “Socrates-Qwen-14B” is provided and no 14B results are reported (Shao et al., 27 Nov 2025).

A broader multimodal precursor is “Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild,” which presents a self-ask, self-answer, consolidate, and summarize pipeline for lightweight MLLMs. Its concrete implementation uses a Vicuna-7B-based LLaVA-style stack with ViT-L/14 and a 2-layer MLP adapter, not Qwen. On the CapQA dataset, the method improves the hallucination score from 69.3 to 90.9, a 31.2\% relative gain, and reaches 93.0 with 3-turn inference. The paper and its detailed mapping note that the method can be adapted to Qwen-14B or Qwen-VL, but this remains an adaptation recipe rather than a reported Socrates-Qwen-14B checkpoint (Hu et al., 6 Jan 2025).

Taken together, these homonymous and adjacent systems indicate that “Socrates-Qwen-14B” is best treated as a contextual designation whose meaning depends on the surrounding paper. In the literature surveyed here, the only fully specified and released instance is the Qwen2.5-14B-Instruct model fine-tuned for human-behavior prediction in social science experiments (Kolluri et al., 6 Sep 2025). Elsewhere, the term functions as a proposed Qwen-14B realization of a Socratic pedagogy, benchmark role, critique model, or multimodal reasoning framework rather than a single shared model lineage.