Self-Feeding Chatbot Framework

• Self-feeding chatbot frameworks are systematic methods that continuously refine dialogue agents by integrating live user feedback and dynamic data acquisition.
• They employ multitask neural networks, adversarial feedback modules, and symbolic reasoning to enhance response quality and ensure computational efficiency.
• By leveraging lifelong learning, preference tuning, and dynamic user profiling, these frameworks enable chatbots to adapt seamlessly in real-world deployments.

A self-feeding chatbot framework is a systematic approach that enables dialogue agents to autonomously and continuously improve themselves during deployment by harvesting and integrating new training signals from ongoing user interactions. This paradigm moves beyond static, pre-deployment training methods by leveraging user responses, satisfaction feedback, corrective interaction, and dynamic adaptation to refine both conversational abilities and knowledge representation. Self-feeding chatbot systems span a range of architectures, including multitask neural networks, lifelong learning paradigms, generative adversarial processes for feedback assimilation, intention-guided response frameworks, efficient ensemble tuning, symbolic reasoning pipelines, and dynamic user profiling via LLMs.

1. Architectural Principles and Joint Task Formulation

The canonical self-feeding chatbot, as described in "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!" (Hancock et al., 2019), is architected with a clear separation between interface and model layers. The interface conducts pre-processing, candidate response preparation, and manages control logic for dynamic example extraction during interactions. The model component encapsulates one or more neural networks trained simultaneously on three tasks:

• Dialogue Task: Predicting subsequent, context-appropriate utterances $(x, y)$
• Satisfaction Task: Scalar estimation of user satisfaction $(x, s)$, $s \in [0,1]$
• Feedback Task: Predicting corrective feedback $(x, f)$

At deployment, a satisfaction threshold $(t)$ discriminates between high-satisfaction turns (user responses are imitated as new human-bot dialogue examples) and low-satisfaction turns (feedback is solicited and saved for auxiliary training). The multitask learning framework cycles through batches of single-task samples, and per-task loss scaling is tuned on validation data.

2. Ongoing Data Acquisition and Feedback Integration

Dynamic data extraction lies at the core of self-feeding. As outlined in (Hancock et al., 2019), and expanded in lifelong learning contexts (Liu et al., 2020), the chatbot system operates as follows:

• High satisfaction $(\hat{s} > t)$: The next user utterance is harvested as an imitation target (human-bot dialogue sample).
• Low satisfaction $(\hat{s} \leq t)$: The chatbot prompts for feedback ("What should I have said?"), acquiring user-generated corrections, paraphrases, or explicit instructions.

Feedback is not always directly usable: it may be noisy, indirect, or instructional. The generative adversarial Feed2Resp module (Sreedhar et al., 2020) style-transfers feedback into plausible, contextually appropriate dialog responses before adding them to the training set. This process involves a BART-based generator $(g_\theta)$ and a Transformer discriminator $(d_\phi)$, with objectives for self-reconstruction, cycle consistency, and style modification: $$\text{Loss}_{self}(\theta) = -\log p_\theta(\hat{y}=x \mid x, h, s)$$

$$\text{Loss}_{cycle}(\theta) = -\log p_\theta(y=x \mid g_\theta(x, h, \hat{s}), h, s)$$

$$\text{Loss}_{style}(\theta) = -\log p_\phi(c=\hat{s} \mid g_\theta(x,h,\hat{s}))$$

3. Lifelong Knowledge Acquisition and Adaptive Functional Expansion

Self-feeding frameworks are explicitly designed for lifelong learning (Liu et al., 2020). During a dialogue, the agent identifies unknown facts, ambiguous references, or missing information:

• Passive Extraction: Mining user utterances for candidate facts and immediately linking novel facts to the knowledge base (KB).
• Active Clarification: Soliciting explicit information when encountering unknown entities, or launching verification sub-dialogues.

New facts, mappings, or skills are first buffered as unverified knowledge until cross-validated (e.g., confirmation by $K$ independent users): $$\text{If count(verification}(f)) \geq K,\, \text{then commit } f \text{ to KB}$$ Revision mechanisms and active learning strategies are deployed to minimize errors and detect contradictions within the evolving KB. For command mapping, user demonstrations and selection from candidate action lists are used to ground natural language expressions, expanding the agent’s functional capabilities.

4. Preference-Tuning and Computational Efficiency

Recent advances in preference alignment, notably LoRA-LiteE (Yang et al., 15 Nov 2024), exploit supervised fine-tuning (SFT) in conjunction with low-rank adaptation (LoRA) and ensemble learning. Rather than relying on resource-heavy RLHF cycles, LoRA-LiteE parameterizes weight layer updates via low-rank matrices $(\Delta W = A\cdot B)$: $W' = W + \Delta W$ Multiple lightweight models (e.g., Gemma-2-9b, Llama-3-8b) are fine-tuned separately and ensembled using weighted averaging: $$P_{final} = 0.7 \cdot P_{gemma} + 0.3 \cdot P_{llama}$$ This enables efficient, frequent re-training and online updating essential for self-feeding chatbots, allowing rapid convergence under resource constraints and competitive performance (80.2% accuracy and 0.99 log loss on the Chatbot Arena benchmark).

5. Structured Reasoning and Symbolic Control

Reliability and robustness in conversational agents are advanced via symbolic reasoning systems such as LLM-ASP frameworks (Zeng, 13 Feb 2025). Here, LLMs serve as front-end parsers—translating user utterances into structured predicates—which are reasoned over by Answer Set Programming modules:

• Parsing: NL input $\to$ predicate forms (e.g., $\text{require}('price\,range',['cheap'])$)
• Reasoning: ASP applies domain rules for consistency, completeness, and context (e.g., $$\text{missing}(X) \leftarrow \text{require}(X,\_)\land \text{not provided}(X)$$)
• Generation: Reasoned predicates $\to$ NL output via LLM

This pipeline ensures that the logical backbone of dialogue is rule-governed and not subject to LLM hallucinations; updates and self-training are mediated via trainer sub-agents that expand predicate structures and reasoning rules in new domains.

6. Implicit Dynamic User Profiling and Personalization

Self-feeding is also realized via implicit, dynamic profiling, as demonstrated by ProfiLLM (David et al., 16 Jun 2025). The framework leverages taxonomic subdomain assignment and recursive weighting to estimate users' technical proficiency without explicit surveys. For each prompt $i$ in subdomain $l$: $$p^l_i = \alpha^l_i \cdot p^{(l,temp)}_i + (1-\alpha^l_i)\cdot p^l_{(i-1)}$$ with temporal attenuation,

$\alpha^l_i = \frac{\alpha_0}{1 + \beta \cdot i}$

Early utterances have outsized influence on the profile; subsequent turns gradually stabilize the score. This enables rapid adaptation (reducing gap between actual and predicted scores by 55–65%) and fine-grained response adjustment, as well as rich persona simulation for synthetic data creation and evaluation.

7. Evaluation Paradigms and Empirical Findings

Empirical evaluation spans multiple dimensions:

• Dialogue Response Ranking (HITS@1/20): Models trained with deployment and feedback examples consistently outperform static supervised models; feed2resp-modified feedback boosts PolyEncoder accuracy from 69.94% to 75.96% on PersonaChat (Sreedhar et al., 2020, Hancock et al., 2019).
• Preference Prediction (Accuracy, Log Loss): LoRA-LiteE delivers competitive performance vs. much larger RLHF-tuned models (Yang et al., 15 Nov 2024).
• Human Trials and Simulation: Guiding chatbots trained to respond with intention (e.g., sentence length, target emotion) can induce measurable effects in human interlocutors—though intention maximization may trade-off with conversational relevance (Su et al., 2021).
• Profiling Accuracy (MAE): ProfiLLM yields rapid, stable proficiency assessment with domain-adaptable taxonomy (David et al., 16 Jun 2025).

8. Prospects, Limitations, and Future Research

Prominent directions include adaptive feedback elicitation, joint data collection, meta-learning strategies for self-improvement, online learning and frequent retraining, expansion to broader domains, and continuous revision protocols for knowledge correctness. Scalability is addressed by modular rule systems (ASP), trainer chatbots for function acquisition, and efficient low-rank ensemble adaptation. Limitations include potential misalignment between reward optimization and conversational relevance, risk of error in unsupervised knowledge acquisition, and computational resource constraints in frequent model updating.

Self-feeding chatbot frameworks represent an overview of imitation learning, feedback-driven self-correction, lifelong knowledge acquisition, dynamic preference alignment, symbolic reliability, and adaptive personalization. By integrating these facets, the emerging paradigm supports robust, continuously improving dialogue agents that leverage deployment interactions as a rich substrate for autonomous evolution and performance enhancement.