Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Abstract: The emergent few-shot reasoning capabilities of LLMs have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating LLMs' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}.

• The paper shows that LLMs rely on semantic patterns rather than explicit symbolic rules for in-context reasoning.
• It employs synthetic datasets like Symbolic Tree and ProofWriter to demonstrate that reasoning performance degrades without commonsense semantic grounding.
• The results suggest that integrating non-parametric knowledge could enhance LLM reasoning, guiding future improvements in AI.

An Analytical Overview of "LLMs are In-Context Semantic Reasoners rather than Symbolic Reasoners"

Introduction

The investigation conducted in the paper by Tang et al., titled "LLMs are In-Context Semantic Reasoners rather than Symbolic Reasoners," offers a detailed exploration into the underlying mechanisms of LLMs during reasoning tasks. The authors interrogate the assumption that LLMs naturally acquire reasoning abilities akin to human processes, such as induction, deduction, and abduction, through an emergent property of their design and training data.

Key Hypothesis and Research Question

The authors propose a critical hypothesis: LLMs predominantly function as semantic reasoners rather than symbolic reasoners. This implies that the LLMs leverage semantic patterns embedded within language tokens to construct a superficial logical chain, rather than performing genuine symbolic computations evident in human reasoning. The central research question raised is whether LLMs retain their reasoning capacities when stripped of the semantic context.

Methodological Approach

The work methodically separates semantic understanding from symbolic reasoning in LLMs by utilizing two synthetic datasets: the Symbolic Tree and ProofWriter datasets. These datasets provide controlled environments where relations and entities can be restructured, allowing the authors to test the reasoning capabilities of LLMs under both semantic-rich and reduced contexts. The experimental evaluations leverage the advanced capabilities of models such as ChatGPT, GPT-4, and LLaMA.

Results and Observations

The results distinctly show that when semantics are consistent with commonsense knowledge, LLMs perform considerably well. For instance, tasks that preserve semantic meaning tend to yield superior inductive and deductive reasoning performance as opposed to when symbols replace semantics, where performance notably declines. Moreover, experiments indicate that the zero-shot reasoning abilities of LLMs are nearing those with chain-of-thought (CoT) training enhancements; this suggests intrinsic abilities to utilize embedded semantic relationships.

In contrasting configurations, when semantics are counter to commonsense (i.e., counter-commonsense scenarios) or they lack clear semantic grounding (as in synthetic tasks like ProofWriter), LLMs struggle to achieve similar levels of performance using the new in-context knowledge presented.

Implications and Discussions

The paper highlights a significant implication: semantic connections within LLMs greatly contribute to their perceived reasoning capabilities. This suggests that advancements in LLMs should remain cautious in overselling symbolic reasoning capabilities when in essence, many successes could be attributed to a semantic-based approach.

In practical applications, the integration of non-parametric knowledge bases is suggested as a promising avenue to bolster the reasoning performance of LLMs. Such hybrid systems would ensure updated and consistent knowledge, a notable advantage highlighted during knowledge addition and updating tasks, wherein approaches such as Neo4j proved more efficient and reliable compared to standard LLM memorization.

Future Directions

Acknowledging the limitations and findings, the paper points towards future research possibilities. These include developing more sophisticated datasets to further segregate semantic and symbolic reasoning capabilities and combining LLMs with systematic approaches that incorporate structured, non-parametric knowledge bases.

Conclusion

Overall, Tang et al.'s work provides essential traction in understanding the true nature of reasoning within LLMs. It prompts a reevaluation of semantic versus symbolic reasoning roles, emphasizing semantics' undeniable impact on in-context learning outcomes. These findings encourage continued exploration into more adaptable and semantically-rich AI systems that can better mimic complex human reasoning processes.