Papers with controlled setting
Exploring the Role of Prior Beliefs for Argument Persuasion (N18-1)
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| Challenge: | Recent studies in natural language processing (NLP) have shown that the language of opinion holders and their patterns of interaction play a key role in changing the mind of a reader. |
| Approach: | They propose to use a dataset to study the effect of language use vs. prior beliefs on persuasion in a controlled setting that takes into account political and religious ideology. |
| Outcome: | The proposed controlled setting takes into account political and religious ideology and shows that prior beliefs play a more important role than language use effects. |
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
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Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
| Approach: | They propose a task of predicting from the very start of a conversation whether it will get out of hand. |
| Outcome: | The proposed framework can detect early warning signs of antisocial behavior in online conversations. |
To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples (2026.acl-long)
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| Challenge: | Chain-of-thought (CoT) prompting and in-context learning (ICL) have unlocked significant reasoning capabilities in large language models (LLMs). |
| Approach: | They propose a meta-training technique to learn reasoning tasks in-context using CoT examples. |
| Outcome: | The proposed methods improve performance on novel reasoning tasks even when there are no CoT examples available in-context. |
The Strawberry Problem: Emergence of Character-level Understanding in Tokenized Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models fail at simple character-level tasks due to low mutual information, study finds . authors propose a lightweight architectural modification that improves character- level reasoning . |
| Approach: | They propose a lightweight architectural modification that improves character-level reasoning while preserving the inductive advantages of subword models. |
| Outcome: | The proposed model improves character-level reasoning while preserving the advantages of subword models. |
Low-Bit Quantization Favors Undertrained LLMs (2025.acl-long)
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| Challenge: | Larger models or those trained on fewer tokens exhibit less quantization-induced degradation (QiD), while smaller, well-trained models face significant performance losses. |
| Approach: | They propose to use QiD to measure an LLM’s training levels and determine the number of training tokens required for fully training LLMs of various sizes. |
| Outcome: | The proposed scaling laws can predict the quantization performance of different-sized LLMs trained with tokens. |
Unlocking Human-Like Visible Logic: How Logic Diagrams Boost Logic Reasoning in Large Language Models? (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated their remarkable capabilities in natural language understanding and generation, but they struggle with formal logical reasoning. |
| Approach: | They propose to incorporate visual logic diagrams into LLMs’ reasoning workflows to enhance their performance on formal logic tasks. |
| Outcome: | The proposed model improves on syllogistic and conditional reasoning with programmatically generated Venn, Euler, and Linear diagrams. |