9 accepted papers
Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous neuro-symbolic methods can be summarized into a two-stage framework: first translating natural language (NL) problems into symboli
Large language models (LLMs) struggle with complex logical reasoning. Previous work has primarily explored single-agent methods, with their performance remains fundamentally limited by the capabilities of a single model. To our knowledge, this paper first introduce a multi-agent approach specificall…
Large language models (LLMs) have achieved impressive progress in natural language processing tasks but still struggle with complex logical reasoning. We observe that in propositional logic question-answering (QA), LLMs
Logical consistency, the requirement that statements remain non-contradictory under logical rules, is fundamental for trustworthy reasoning, yet current LLMs often fail to maintain it even on simple inference tasks. Existing benchmarks for LLM logical consistency are not scalable, not diverse, and n…
Large Language Models (LLMs) often struggle with complex logical reasoning. Existing approaches typically rely on either purely neural reasoning in natural language or offloading to formal solvers via symbolic representations. However, both paradigms face significant limitations: while LLMs exhibit …
Large Language Models (LLMs) demonstrate remarkable performance across various natural language processing tasks but struggle with complex logical reasoning, particularly in real-world settings. Existing research is largely confined to the closed-world assumption, which posits that all premises requ…
Large language models (LLMs) have achieved remarkable successes on various tasks. However, recent studies have found that there are still significant challenges to the logical reasoning abilities of LLMs, which can be categorized into the following two aspects: (1) Logical question answering: LLMs o
Large language models (LLMs) have demonstrated impressive progress in various natural language progress tasks. However, it has been observed that LLMs still struggle with complex causal and logical reasoning. To facilitate this research direction, we first proposed a training method to distinguish c
Identifying causal relationships rather than spurious correlations between words and class labels plays a crucial role in building robust text classifiers. Previous studies proposed using causal effects to distinguish words that are causally related to the sentiment, and then building robust text cl