Papers with C
Does BERT Know that the IS-A Relation Is Transitive? (2022.acl-short)
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| Challenge: | Recent studies suggest pre-trained BERT can capture lexico-semantic clues from words in context. |
| Approach: | They examine word senses and the transitive property of IS-A relation . they aim to quantify how much BERT agrees with transitivity property . |
| Outcome: | The proposed model can capture lexico-semantic clues from words in context . but to what extent it captures transitive nature of some lexical relations is unclear . |
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes (2024.naacl-long)
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| Challenge: | Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes. |
| Approach: | They propose a method that uses cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation. |
| Outcome: | The proposed model can bridge the novice-expert knowledge gap by using cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation. |
Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation (2026.findings-eacl)
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| Challenge: | Large Language Model (LLM) judges are limited to textual content, resulting in expensive and opaque evaluation methods. |
| Approach: | They propose a framework that enables large language model judges to reason over audio cues . they introduce a human chain-of-thought annotation protocol to improve judge diagnostic capability . |
| Outcome: | The proposed framework achieves higher agreement with human raters than ALMs and transcript-only LLM judges while being significantly more cost-effective. |
Learning Features from Co-occurrences: A Theoretical Analysis (C18-1)
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| Challenge: | Existing theories for word classification and clustering are lacking. |
| Approach: | They propose a theory that uses a function to represent a word by its co-occurrences with other words in context. |
| Outcome: | The proposed model improves word classification and clustering by using multiple features. |
SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent (2025.naacl-long)
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Keyeun Lee, Seo Hyeong Kim, Seolhee Lee, Jinsu Eun, Yena Ko, Hayeon Jeon, Esther Hehsun Kim, Seonghye Cho, Soeun Yang, Eun-mee Kim, Hajin Lim
| Challenge: | Existing methods for simulating individual identities oversimplify human complexity, leading to incomplete or flattened representations. |
| Approach: | They propose a framework for constructing authentic LLM agent personas by incorporating an individual’s multidimensional self-concept. |
| Outcome: | The framework integrates Social Identity (S), Personal Identity (P), and Personal Life Context (C) components, each contributing distinct yet interconnected aspects of identity. |
Ask Question First for Enhancing Lifelong Language Learning (2022.coling-1)
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| Challenge: | Existing approaches to stream learning NLP tasks suffer from catastrophic forgetting and are exacerbated when the previous task’s pseudo data is insufficient. |
| Approach: | They propose to use a new data format to train pseudo questions of previous tasks to stream learning NLP tasks while retaining knowledge of previous ones. |
| Outcome: | The proposed model is more robust to sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. |
Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (2022.lrec-1)
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| Challenge: | Pre-trained Transformer models have proven their effectiveness in adapting to multiple NLP tasks and domains. |
| Approach: | They evaluated three categories of out-of-vocabulary words using three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. |
| Outcome: | The proposed models can create new representations for out-of-vocabulary words by adding external morpho-syntactic context rather than improving the semantic understanding of the words directly. |
On General Language Understanding (2023.findings-emnlp)
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| Challenge: | a recent paper suggests that the evidence underspecifies the understanding of large language models. |
| Approach: | They propose to use a "general language understanding" benchmark to examine what it could mean in machines. |
| Outcome: | The proposed model can be used to ground questions of the adequacy of benchmarking methods. |