Papers with machine reasoning
Machine Reasoning: Technology, Dilemma and Future (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | Machine reasoning is a field of research that aims to build interpretable AI systems . Symbolic reasoning methods represent knowledge using symbolic logic, perform inference . probabilistic reasoning methods combine probability and symbolic logic into a unified model . |
| Approach: | This tutorial introduces machine reasoning frameworks and aims to define them . it will show how they can be used to build interpretable AI systems . |
| Outcome: | This tutorial aims to show how machine reasoning frameworks perform in real-world scenarios. |
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning (2020.emnlp-main)
Copied to clipboard
| Challenge: | XCOPA dataset provides a typologically diverse dataset for commonsense reasoning in 11 languages . current methods for evaluating commonsensible reasoning in resource-poor languages are weak compared to translation-based transfer. |
| Approach: | They propose a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. |
| Outcome: | The proposed model performs better than current methods on a resource-poor dataset compared to translation-based transfer in the 11 languages studied . |
Could you give me a hint ? Generating inference graphs for defeasible reasoning (2021.findings-acl)
Copied to clipboard
| Challenge: | Defeasible reasoning is a mode of reasoning where conclusions can be overturned by taking into account new evidence. |
| Approach: | They propose to automatically generate inference graphs for a defeasible inference task by transfer learning from a related NLP task. |
| Outcome: | The proposed method generates meaningful graphs for a defeasible inference task and human accuracy improves by 20%. |
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Counterspeech, i.e. responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship risks of deletion-based content moderation. |
| Approach: | They draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. |
| Outcome: | The strategies used in human- and machine-generated counterspeech datasets are convincing, whereas human-written counterspech uses less specific strategies compared to machine-produced counters. |
Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks (2026.acl-long)
Copied to clipboard
| Challenge: | Abstraction and Reasoning Corpus and ARC-AGI are widely used to assess progress in artificial intelligence. |
| Approach: | They propose a two-stage pipeline that separates perception and reasoning . they propose to test this pipeline against standard end-to-end one-stage evaluation . |
| Outcome: | The proposed pipeline separates perception and reasoning, and isolates reasoning from bottlenecks. |
Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)
Copied to clipboard
Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou
| Challenge: | Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured. |
| Approach: | They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory. |
| Outcome: | The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems. |
Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities . |
| Approach: | They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query . |
| Outcome: | The proposed framework addresses embedding of emerging entities through contextual information aggregation. |