Papers with in-
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models (2024.eacl-long)
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| Challenge: | Recent studies have indicated that NLI models have an understanding of lexical and compositional semantics. |
| Approach: | They propose a framework to assess the extent of semantic sensitivity in NLI models . they use adversarially generated examples with minor semantics-preserving surface-form variations . |
| Outcome: | The proposed framework shows that NLI models struggle with minor variations requiring knowledge of compositional semantics . |
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (2024.naacl-long)
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| Challenge: | Existing methods to generate counter-misinformation responses are often trained end-to-end without external knowledge, resulting in subpar text quality and excessively repetitive responses. |
| Approach: | They propose retrieval augmented response generation for online misinformation (RARG) that collects supporting evidence and generates counter-misinformation responses via reinforcement learning from human feedback. |
| Outcome: | The proposed method outperforms baselines with extensive experiments with in- and cross-domain datasets and consistently generates high-quality counter-misinformation responses. |
Rethinking Semi-supervised Learning with Language Models (2023.findings-acl)
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| Challenge: | Semi-supervised learning (SSL) is a popular setting to make use of unlabelled data . Currently, there are two popular approaches to make effective use of the unlabelled datasets . |
| Approach: | They compare semi-supervised learning (SSL) and task-adaptive pre-training (TAPT) they find TAPT is a stronger and more robust SSL learner, even when using just a few hundred unlabelled samples . |
| Outcome: | The proposed methods improve model performance across different NLP tasks and data sizes. |
Evaluating Shortest Edit Script Methods for Contextual Lemmatization (2024.lrec-main)
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| Challenge: | Modern contextual lemmatizers often rely on automatically induced Shortest Edit Scripts (SES) supervised contextual methods are used to perform lemma classification tasks. |
| Approach: | They propose to use masked language encoders to compute shortest edit Scripts (SES) SES is the number of edit operations to transform a word form into its lemma . |
| Outcome: | The proposed model outperforms language-specific models in all evaluation settings with seven languages of different morphological complexity. |
LM2: A Simple Society of Language Models Solves Complex Reasoning (2024.emnlp-main)
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| Challenge: | Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning. |
| Approach: | They propose a language-based decomposition, solution and verification framework that modularizes the decomposer, solution, and verification into three different language models. |
| Outcome: | The proposed model outperforms existing methods on in- and out-domain reasoning problems, outperforming the best baselines by 8.1% on MATH, 7.71% on JEEBench, and 9.7% on MedQA problems. |