Enhancing Interpretability and Control in Language Models

The recent research in language models (LMs) has seen a shift towards more nuanced understanding and control of model behaviors, particularly in handling factual information and knowledge conflicts. Innovations are focusing on developing methods to interpret and manipulate the internal processes of LMs to better align with desired outcomes, such as accurate fact completion and resolution of knowledge discrepancies. Techniques like causal tracing and representation engineering are being employed to dissect and influence how LMs process information, aiming to bridge the gap between training and inference behaviors. Additionally, there is a growing emphasis on creating benchmarks and datasets to systematically evaluate and improve LM performance in scenarios involving conflicting knowledge sources. These advancements collectively aim to enhance the reliability and interpretability of LMs, making them more robust and trustworthy in practical applications.

Noteworthy papers include one that introduces a model-specific recipe for constructing datasets to facilitate precise interpretations of LMs for fact completion, and another that proposes training-free representation engineering methods to control knowledge selection behaviors in LLMs, showing significant improvements in resolving knowledge conflicts.

Sources

Fact Recall, Heuristics or Pure Guesswork? Precise Interpretations of Language Models for Fact Completion

Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens

Who's Who: Large Language Models Meet Knowledge Conflicts in Practice

Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering

Analysing the Residual Stream of Language Models Under Knowledge Conflicts

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