Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable generalization abilities in mathematics and reasoning tasks. |
| Approach: | They pre-trained decoder-based language models from scratch using ten programming languages and three natural language datasets. |
| Outcome: | The proposed models outperform natural languages on logical reasoning tasks. |
Similar Papers
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024.acl-long)
Copied to clipboard
Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
| Challenge: | Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics. |
| Approach: | They propose to use a natural language question-answering dataset to evaluate the logical reasoning ability of large language models. |
| Outcome: | The proposed model performs poorly on a range of natural language questions using chain-of-thought prompting. |
Exploring Self-supervised Logic-enhanced Training for Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Traditional attempts to enhance the logical reasoning abilities of language models often rely on supervised fine-tuning, limiting their generalization to new tasks or domains. |
| Approach: | They propose a framework for integrating logical reasoning capabilities into LLMs and activating them via in-context learning. |
| Outcome: | The proposed framework achieves comparable results to existing models on three language understanding benchmarks. |
Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited. |
| Approach: | They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages. |
| Outcome: | The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages. |
What do Large Language Models Learn beyond Language? (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pretraining on text confers models with useful ‘inductive biases’ for non-linguistic reasoning. |
| Approach: | They investigate whether pre-training on text confers these models with helpful ‘inductive biases’ for non-linguistic reasoning. |
| Outcome: | The proposed models outperform non-pretrained models on 19 non-linguistic tasks and show that they retain inductive biases even when training on multi-lingual text and computer code. |
Can Pretrained Language Models (Yet) Reason Deductively? (2023.eacl-main)
Copied to clipboard
| Challenge: | Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks. |
| Approach: | They conduct a comprehensive evaluation of the learnable deductive reasoning capability of pretrained language models and compare their performance against simple adversarial surface form edits. |
| Outcome: | The models are able to generalise learned logic rules and perform inconsistently against simple adversarial surface form edits, but catastrophically forget the previously learnt knowledge. |
oLMpics-On What Language Model Pre-training Captures (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent success of pre-trained language models has spurred widespread interest in their capabilities. |
| Approach: | They propose an evaluation protocol that includes zero-shot evaluation and no fine-tuning . they propose to compare the learning curve of a fine- tuned LM to the learning of multiple controls . |
| Outcome: | The proposed evaluation protocol compares the learning curve of a fine-tuned LM to the learning of multiple controls. |
Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
Copied to clipboard
Yuxuan Wan, Wenxuan Wang, Yiliu Yang, Youliang Yuan, Jen-tse Huang, Pinjia He, Wenxiang Jiao, Michael Lyu
| Challenge: | LogicAsker examines and improves the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Approach: | They propose a set of atomic reasoning skills grounded in propositional and predicate logic to examine and improve the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Outcome: | The proposed approach improves reasoning abilities in large language models such as ChatGPT and GPT-4 by up to 5%. |
LogicNMR: Probing the Non-monotonic Reasoning Ability of Pre-trained Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work examines the non-monotonic reasoning ability of pre-trained language models. |
| Approach: | They construct a non-monotonic reasoning benchmark with explicit default rules and iterative updates. |
| Outcome: | The proposed model achieves a higher accuracy than the benchmark, but performs poorly on the benchmark. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
Copied to clipboard
| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |