Content Effects in LLMs

• Content effects in LLMs are systematic influences from semantic cues like plausibility and sentiment, impacting reasoning and output quality.
• They emerge from large-scale training data that conflates logical validity with content attributes, measurable through techniques like cosine similarity.
• Mitigation strategies such as representational debiasing and activation steering offer practical interventions to balance biases and enhance model performance.

Content effects in LLMs refer to the systematic influence that semantic content—such as plausibility, sentiment, informativeness, or sensitive language—exerts on model outputs across reasoning, content generation, moderation, retrieval, and user interaction. These effects arise from the models’ internal representations, their reliance on large and complex training corpora, and their emergent behaviors, often yielding biases or sensitivities analogous to those observed in human cognition. Recent research has enumerated, dissected, and—critically—proposed mitigation strategies for content effects across diverse scenarios, from logical reasoning to information retrieval, content moderation, and social simulation.

1. Representational Mechanisms and the Conflation of Logical and Content Effects

Extensive analysis demonstrates that LLMs represent abstract concepts, such as logical validity and plausibility, as nearly collinear directions in activation space, leading to their conflation (Bertolazzi et al., 8 Oct 2025). For a given transformer layer $l$, the direction for “validity” is defined as:

$v_{\text{validity}}^l = \mu_{v^+}^l - \mu_{v^-}^l$

where $\mu_{v^+}^l$ and $\mu_{v^-}^l$ are mean hidden activations for “valid/invalid” predictions. Similarly, plausibility is encoded by $v_{\text{plausibility}}^l$. Cosine similarity between these vectors is high (typically $0.48$–$0.64$ in steerable layers), indicating strong geometric alignment. The practical outcome is that plausibility cues causally bias validity judgments (and vice versa) when applied as steering vectors, directly influencing output predictions. Thus, behavioral “content effects” in LLMs (e.g., judging logically invalid arguments as valid if their content is plausible) are predictable from this representational entanglement. This mechanism is robust to varying prompting regimes (zero-shot vs. chain-of-thought), although zero-shot prompting reveals higher sensitivity. The degree of alignment quantitatively predicts the size of the behavioral content effect across models and prompts (Bertolazzi et al., 8 Oct 2025).

2. Quantitative Decomposition of Content Effects: Memorization vs. In-Context Reasoning

An axiomatic decomposition formalizes LLM output as a sum of memorization and in-context reasoning effects (Lou et al., 20 May 2024). The confidence score for predicting the next token is:

$$v(x_{n+1}|\mathbf{x}) = \log\frac{p(x_{n+1}|\mathbf{x})}{1 - p(x_{n+1}|\mathbf{x})}$$

This is decomposed into token interactions $I(S|\mathbf{x})$, where $S \subseteq \Omega$ indexes subsets of input tokens. Each interaction splits into memorization ($\mathcal{J}(S|\mathbf{x})$) and reasoning effects ($\mathcal{K}(S|\mathbf{x})$) according to:

$$I(S|\mathbf{x}) = \mathcal{J}(S|\mathbf{x}) + \mathcal{K}(S|\mathbf{x})$$

Memorization further divides into foundational and chaotic types, while reasoning effects capture the impact of new context, classified as enhanced, eliminated, or reversed inference patterns. Empirically, foundational memorization spans all interaction orders, but reasoning effects mainly suppress high-order (less generalizable) interactions and modulate low-order ones. Importantly, only a sparse subset of possible interactions meaningfully contributes (i.e., the sparsity property), and universal matching ensures all masked input variants can be reconstructed additively.

3. Empirical Manifestations: Biases, Hallucinations, Moderation, and Similarity Judgments

Multiple dimensions of content effects have been experimentally quantified:

• Framing and Sentiment Bias: LLM-generated summaries alter the sentiment framing of input contexts in ~21.86% of cases, as measured by the fraction of outputs $$BI = (1/n)\sum_{i=1}^n \mathbb{1}(f_c^{(i)} \neq f_s^{(i)})$$, where $f_c$ and $f_s$ are the sentiment labels of context and summary, respectively (Alessa et al., 3 Jul 2025).
• Primacy Bias: Models overemphasize early portions of text, measured by comparing chunk similarity scores; primacy bias occurs in ~5.94% of instances (Alessa et al., 3 Jul 2025).
• Hallucination: On post-knowledge-cutoff queries, hallucination rates can reach 57.33% (Alessa et al., 3 Jul 2025).
• Order and Context Effects: In similarity judgments, the order of items affects LLM outputs, mirroring human (Tversky–Gati-type) asymmetries. Biases are statistically quantified using paired $t$-tests on $\{s(a,b), s(b,a)\}$ and are model-, temperature-, and prompt-sensitive (Uprety et al., 20 Aug 2024).
• Implicit Moderation: LLMs such as GPT-4o-mini, when paraphrasing sensitive content, shift the sensitivity scale downward even without explicit detoxification instructions (mean shift $\Delta_c < 0$ for “Taboo” and “Derogatory” content classes), indicating an implicit moderation bias (Ferrara et al., 31 Jul 2025).

These biases can have tangible effects in high-stakes scenarios—altered sentiment in medical/legal summaries, primacy effect in evidence selection, and hallucination in knowledge-critical tasks—necessitating rigorous safeguards.

4. Interventions and Mitigation Strategies

Several intervention strategies address content effects at inference and training time:

• Representational Debiasing: Debiasing vectors constructed as mean differences between task representations ($\mu_{V-P}^l = \mu_V^l - \mu_P^l$) can be added to hidden activations to “orthogonalize” validity from plausibility, demonstrably reducing accuracy drops and behavioral content effects (Bertolazzi et al., 8 Oct 2025).
• Activation Steering: Contrastive and conditional activation steering methods (including fine-grained $k$-NN-based steering—$CAST^$) intervene at the activation level to favor formal over content-driven reasoning. This technique yields up to $+15\%$ absolute improvement in formal reasoning accuracy in otherwise unresponsive models, with robust performance under prompt variation and limited collateral impact on language modeling (Valentino et al., 18 May 2025).
• Instruction Tuning for Content Safety: Defense datasets paired with single-task and mixed-task loss functions systematically teach LLMs to refuse dangerous content, balancing safety (blocking malicious texts) and utility (processing benign documents). The trade-off is sensitive to both model family and task formulation; for instance, LLaMA2-7B achieves a better utility-safety balance than LLaMA1-7B under the same regime (Fu et al., 24 May 2024).
• Epistemic Tagging and Prompt-based Awareness: Interventions such as epistemic confidence tagging (“True [High Confidence]”) and explicit knowledge cutoff prompts reduce hallucination rates and improve factual reliability in out-of-distribution and post-training data (Alessa et al., 3 Jul 2025).
• Content Filtering by Informativeness: Self-information-based selective context algorithms rank and filter lexical units to maximize informational density within fixed model context windows. Performance on summarization tasks drops only minimally with 20–35% of well-targeted content removed, although aggressive reduction may degrade QA fidelity (Li, 2023).

5. Broader Systemic Content Effects: Retrieval, Moderation, and Social Simulation

LLMs also shape content exposure in retrieval and automated moderation contexts:

• Generative Search Engine Bias: LLM-driven generative search prefers citing low-perplexity (predictable) and semantically similar content, reinforcing stylistic and topical homogeneity in cited sources; however, LLM-based website content “polishing” paradoxically yields greater diversity of sources in AI summaries due to increased compatibility with the LLM’s generation process (Ma et al., 17 Sep 2025). User studies reveal lower-educated users gain information density, while highly educated users achieve efficiency gains under optimized content paradigms.
• Harm Filtering in Pretraining: Advanced models such as HarmFormer employ multi-dimensional harm taxonomies and task-specific heads to distinguish “Safe,” “Topical,” and “Toxic” content across five harm categories. Adversarial evaluation via HAVOC highlights the persistent risk of toxicity leakage (overall 26.7%, rising to 76% for “provocative leak” scenarios), underscoring the limits of traditional filtering (Mendu et al., 4 May 2025).
• Social Simulation and Social Information Processing: Standard LLMs underperform in stance detection, emotional memory, and goal personalization critical to human-like simulation of information spread. Integrating Social Information Processing-based Chain-of-Thought (SIP-CoT) and emotion-guided memory modules aligns LLM agent behavior more closely with empirical social dynamics, evidenced by increased F1 scores in stance and content alignment and decreased bias/deviation in opinion simulation (Zhang et al., 8 Jul 2025).

6. Impact on Collaborative Knowledge and Norm Formation

The deployment of LLMs in knowledge communities such as Wikipedia changes content creation and community participation norms (Zhou et al., 9 Sep 2025). Experienced editors employ LLMs to extend topic reach and improve workflow efficiency, leveraging tacit editorial knowledge to align AI-generated content with stringent community guidelines on neutrality, verifiability, and no original research. In contrast, newcomers face challenges in evaluating, verifying, and correcting LLM output, frequently resulting in negative community responses and edit rejections. The observed participation divide highlights the necessity for LLM-based tools that not only scaffold content generation but also teach domain-specific norms, dynamically adapting guidance to user expertise.

7. Open Research Challenges and Future Directions

Despite advances in decomposition, mitigation, and representational steering, several challenges remain:

• Multidimensional Bias Mitigation: Most current methods target individual biases (e.g., sentiment, primacy, hallucination, or logical conflation) rather than overlapping or interacting effects. Integrated, user-aware strategies remain an open research frontier (Alessa et al., 3 Jul 2025).
• Data and Evaluation Diversity: Ongoing enrichment of benchmarks—encompassing adversarial, self-updating, and multilingual datasets—will be critical for generalization and robust safety evaluation (Mendu et al., 4 May 2025, Ferrara et al., 31 Jul 2025).
• Dynamic Moderation vs. Stylistic Fidelity: Implicit moderation strategies in state-of-the-art LLMs (driven by RLHF) systematically reduce offensiveness at the cost of fidelity to original style or authorial intent; further research is required to balance moderation with authentic expression (Ferrara et al., 31 Jul 2025).
• Human Cognitive Alignment: Order effects, context sensitivity, and representational parallels to dual-process theories in human reasoning prompt further exploration of how LLMs inherit or diverge from human-like content effects (Uprety et al., 20 Aug 2024, Bertolazzi et al., 8 Oct 2025).
• Causal and Policy Implications: The use of LLMs in modeling and influencing social systems (causal text interventions, information propagation, knowledge production) requires rigorous causal inference tools adapted to high-dimensional, LLM-mediated interventions (Guo et al., 28 Oct 2024).

Content effects in LLMs fundamentally reflect the tension between statistical learning, world knowledge, reasoning logic, and user-valued outcomes. They are central to both the scientific understanding of model behavior and the design of robust, equitable, and contextually aligned language technologies.