Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses (2023.findings-acl)
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| Challenge: | Existing methods to reduce cognitive errors in MRI interpretations do not work for generating less likely outputs. |
| Approach: | They propose a task that asks a model to generate outputs that humans think are relevant but less likely to happen. |
| Outcome: | The proposed method compares with several state-of-the-art controlled text generation models via automatic and human evaluations and shows that it reduces cognitive errors in interpreting MRI findings. |
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Automating Alternative Generation in Decision-Making (2025.findings-emnlp)
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| Challenge: | Cognitive biases can impede decision making by constraining individual decision makers’ creativity. |
| Approach: | They propose a task for automatically generating alternative options based on atomic action components and a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. |
| Outcome: | The proposed task is based on 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. |
A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
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Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning (2023.findings-acl)
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| Challenge: | Current language models have shown impressive capability of generating fluent and grammatical text, but they often produce behaviors misaligned with human expectations. |
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Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation (2026.findings-acl)
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| Challenge: | Language models are accelerating scientific research by automating hypothesis generation and implementation. |
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HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation (2026.acl-long)
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| Challenge: | Existing frameworks for LLM-as-a-judge use zero-shot setting without consulting any human input, which leads to low alignment, or fine-tune LLMs on labeled data, which requires a non-trivial number of samples. |
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The CRINGE Loss: Learning what language not to model (2023.acl-long)
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| Challenge: | Standard language model training uses gold human documents or human-human interaction data and treats all training data as positive examples. |
| Approach: | They propose a procedure to train with negative examples using the "CRINGE" loss technique and use it to train models with such data. |
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Factorising AMR generation through syntax (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
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Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023.eacl-main)
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| Challenge: | Recent advances in the capacity of large language models to generate human-like text have prompted a heated discourse around the risks of societal harms they introduce. |
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A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
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Perceptions of Linguistic Uncertainty by Language Models and Humans (2024.emnlp-main)
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| Challenge: | Prior work has shown that humans are well-attuned to the use of uncertainty expressions, exhibiting population-level agreement in mapping these expressions to numerical responses. |
| Approach: | They propose to map linguistic expressions of uncertainty to numerical responses by using a theory of mind approach to understand the uncertainty of another agent. |
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