Papers with inference tasks
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2023.tacl-1)
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| Challenge: | Existing methods for enhancing data efficiency in limited labeled data are limited. |
| Approach: | They propose to use data augmentation methods to increase the efficiency of limited data learning in NLP. |
| Outcome: | The proposed methods perform well on topics/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. |
Theory of Mind for Multi-Agent Collaboration via Large Language Models (2023.emnlp-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive accomplishments in reasoning and planning, but their abilities in multi-agent collaborations remain unexplored. |
| Approach: | They propose to use explicit belief state representations to enhance task performance and the accuracy of ToM inferences for LLM-based agents. |
| Outcome: | The proposed model improves performance and accuracy of ToM inferences for LLM-based agents. |
Retrofitting Contextualized Word Embeddings with Paraphrases (D19-1)
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| Challenge: | Contextualized word embeddings can be useful for downstream applications, but they can be over-sensitive to contexts. |
| Approach: | They propose a method to retrofit contextualized word embeddings with paraphrases to minimize the variance of word representations on paraphrased contexts. |
| Outcome: | The proposed method improves on sentence classification and inference tasks. |
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model (2022.naacl-main)
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| Challenge: | Existing word-level approaches to attack text are limited to a single word . existing methods ignore interactions between consecutive words, resulting in one-to-one attacks . |
| Approach: | They propose a black-box attack framework that misleads the language model by applying variable-length contextualized transformations to the original text. |
| Outcome: | The proposed framework outperforms existing methods on classification and inference tasks. |
Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed. |
| Approach: | They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer . |
| Outcome: | The proposed model takes context information into consideration, which helps select suitable rules for inference tasks. |
Generating Label Cohesive and Well-Formed Adversarial Claims (2020.emnlp-main)
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| Challenge: | Existing work on adversarial triggers for fact checking models reveals weaknesses and flaws of models . universal adversarials often inadvertently invert the meaning of instances they are inserted in . |
| Approach: | They propose a method for automatically generating highly potent, well-formed, label cohesive claims for FC using universal adversarial triggers. |
| Outcome: | The proposed method maintains the directionality and semantic validity of the claim better than previous work on the FEVER dataset. |
Reanalyzing the Most Probable Sentence Problem: A Case Study in Explicating the Role of Entropy in Algorithmic Complexity (2021.eacl-main)
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| Challenge: | Existing descriptive complexity measures are ineffective at describing algorithms' behaviour, and can make an apparently tractable problem seem NP-complete. |
| Approach: | They propose to use statistical measures to give an updated analysis of the complexity of the NP-complete most probable sentence problem for pCFGs. |
| Outcome: | The proposed method can be applied to word sense disambiguation and inference tasks. |
Task Calibration: Calibrating Large Language Models on Inference Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive zero-shot performance on inference tasks, however, they may suffer from spurious correlations between input texts and output labels, which limits their ability to reason based purely on general language understanding. |
| Approach: | They propose a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation. |
| Outcome: | The proposed calibration method improves on 13 benchmarks and prompt templates and can be integrated with other calibration methods. |
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models (2023.findings-emnlp)
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| Challenge: | Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures. |
| Approach: | They propose a framework that narrows the encoder and shortens the decoder networks of encoder-decoder models. |
| Outcome: | The proposed framework reduces the number of decoder layers and improves generation quality. |
Contrastive Learning for Inference in Dialogue (2023.emnlp-main)
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| Challenge: | Recent large language models show remarkable advances in inference tasks, but their performance in inductive reasoning is far behind deductive reasoning. |
| Approach: | They propose to use negative samples to analyze inferences based on the semantic information gap between dialogue contexts and desired inference. |
| Outcome: | The proposed model improves inference generation by feeding negative samples to the models. |
Nash CoT: Multi-Path Inference with Preference Equilibrium (2024.emnlp-main)
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| Challenge: | Multi-path inference is an improvement on multi-path reasoning, but there is no optimal setting for the number of inference paths. |
| Approach: | They propose to use question-related role templates to guide LLMs into relevant roles to reduce the dependence on the number of inference paths. |
| Outcome: | The proposed system can achieve comparable or better results than self-consistency with the same number of paths. |
Explicit Inductive Inference using Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) suffer a signifi- cant performance drop when entailment labels disagree with the attestation label of hypothesis H. |
| Approach: | They propose a pipeline that exploits an LLM's attestation bias to do explicit inductive inference . they transform a premise into attested alternatives and aggregate the results . |
| Outcome: | The proposed pipeline improves the performance of large language models on inference tasks and alleviates the attestation bias. |
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025.findings-emnlp)
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| Challenge: | Large language models have shown remarkable progress in reasoning abilities and general natural language processing tasks, yet their performance on Arabic data remains underexplored. |
| Approach: | They compare reasoning-focused LLMs with deepSeek models across 15 Arabic NLP tasks . they use zero-shot, few-shot and fine-tuning to evaluate their capacity for linguistic reasoning . |
| Outcome: | The proposed models outperform strong models on Arabic datasets and are compared with other models. |