On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models (2406.10625v2)

Abstract: As LLMs are increasingly being employed in real-world applications in critical domains such as healthcare, it is important to ensure that the Chain-of-Thought (CoT) reasoning generated by these models faithfully captures their underlying behavior. While LLMs are known to generate CoT reasoning that is appealing to humans, prior studies have shown that these explanations do not accurately reflect the actual behavior of the underlying LLMs. In this work, we explore the promise of three broad approaches commonly employed to steer the behavior of LLMs to enhance the faithfulness of the CoT reasoning generated by LLMs: in-context learning, fine-tuning, and activation editing. Specifically, we introduce novel strategies for in-context learning, fine-tuning, and activation editing aimed at improving the faithfulness of the CoT reasoning. We then carry out extensive empirical analyses with multiple benchmark datasets to explore the promise of these strategies. Our analyses indicate that these strategies offer limited success in improving the faithfulness of the CoT reasoning, with only slight performance enhancements in controlled scenarios. Activation editing demonstrated minimal success, while fine-tuning and in-context learning achieved marginal improvements that failed to generalize across diverse reasoning and truthful question-answering benchmarks. In summary, our work underscores the inherent difficulty in eliciting faithful CoT reasoning from LLMs, suggesting that the current array of approaches may not be sufficient to address this complex challenge.

An Analysis of Faithful Chain-of-Thought Reasoning in LLMs

The paper "On the Hardness of Faithful Chain-of-Thought Reasoning in LLMs" presents an investigation into the challenge of ensuring that the Chain-of-Thought (CoT) reasoning generated by LLMs truly reflects their underlying computational behavior. This aspect of LLMs is crucial, especially as these models are increasingly deployed in high-stakes domains such as healthcare and legal advisory, where the trustworthiness of model explanations is paramount.

The authors explore several methods aimed at enhancing the faithfulness of CoT reasoning in LLMs, including in-context learning, fine-tuning, and activation editing. Their empirical analyses, carried out across multiple benchmark datasets, reveal that these approaches generally offer limited success.

Key Findings

1. In-Context Learning (ICL): ICL strategies improve the faithfulness of CoT reasoning but often at the cost of accuracy. The paper discusses different sampling strategies, including deterministic and stochastic methods, highlighting that while stochastic faithful sampling often provides better results, the trade-off between accuracy and faithfulness remains a recurring issue.
2. Fine-Tuning: The authors employ parameter-efficient fine-tuning methods to improve the model's faithfulness. Their results suggest that while fine-tuning using datasets curated with faithful CoT reasoning can enhance faithfulness, it is not sufficient to generalize across diverse datasets. The accuracy-faithfulness balance is hard to achieve, and fine-tuning frequently results in a decreased overall model accuracy.
3. Activation Editing: By probing the attention heads of LLMs, the authors identify certain components of the model more closely aligned with faithful reasoning. Despite the sophisticated probing and activation manipulation strategies, the improvements in faithfulness were marginal. The findings suggest that intervening directly in the internal representations of LLMs is a highly complex task that does not always yield significant benefits.

Implications

The research underscores the inherent difficulty of extracting faithful CoT reasoning from LLMs using current methodologies. The marginal success of these attempts emphasizes the gap in existing techniques and the need for novel methods or enhanced theoretical frameworks to accurately capture and reflect the decision-making processes of LLMs.

The implications are profound for AI deployment in critical sectors. Faithful reasoning can enhance trust in AI systems, enabling stakeholders to make informed decisions based on the explanations provided by these models. Conversely, the lack of reliable faithfulness makes it challenging for decision-makers to fully rely on AI outputs, potentially leading to skepticism or misuse.

Future Directions

The paper suggests a roadmap for future research that includes:

• Developing new metrics and tools to quantify faithfulness more effectively.
• Investigating alternative machine learning paradigms or architectural modifications that inherently prioritize faithfulness.
• Further dissecting the internal structures of LLMs to better understand which aspects govern their reasoning processes.

In conclusion, while the methods explored provide limited improvements, the paper serves as a valuable reference point for future development in enhancing the faithfulness of explanations generated by LLMs. The work highlights the critical need for continued research and innovation in this area to equip AI systems with the necessary capabilities to accurately and reliably explain their predictions.