Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce (2025.emnlp-main)
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| Challenge: | Autoregressive neural language models (LMs) generate a probability distribution over tokens at each time step given a prompt. |
| Approach: | They propose to find a prompt that induces LMs to output a distribution as close as possible to the target, using either soft or hard gradient-based prompt tuning. |
| Outcome: | The proposed model is able to generate a distribution as close as possible to a target given a prompt, and it can be used to approximate distributions with low or high entropy. |
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Demystifying optimized prompts in language models (2025.emnlp-main)
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| Challenge: | Modern language models (LMs) are not robust to out-of-distribution inputs. |
| Approach: | They investigate the composition of machine generated (“optimized”) prompts and the mechanisms by which LMs parse and build predictions from them. |
| Outcome: | The proposed prompts are primarily composed of punctuation and noun tokens, which are more rare in the training data. |
How to Compute the Probability of a Word (2024.emnlp-main)
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| Challenge: | Language models estimate a probability distribution over strings in a natural language . many recent linguistic studies have been incorrectly computing word probabilities . |
| Approach: | They propose to use the correct method to compute word probabilities . they highlight issues when relying on models that use end-of-word tokenisers . |
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How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction (2021.emnlp-main)
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| Challenge: | Using RNN and transformer language models, we show consistent generalization in out-of-distribution contexts. |
| Approach: | They propose two idealized models of generalization in next-word prediction . they show that neural language models interpolate between these two forms of generalisation . |
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How Can We Know What Language Models Know? (2020.tacl-1)
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| Challenge: | Recent work examines knowledge contained in language models by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. |
| Approach: | They propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. |
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Unnatural language processing: How do language models handle machine-generated prompts? (2023.findings-emnlp)
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| Challenge: | Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are outperformed by automatically generated token sequences with no apparent meaning or syntactic structure. |
| Approach: | They propose to use machine-generated prompts to probe how models respond to input that is not composed of natural language expressions. |
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Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
Reliability of Distribution Predictions by LLMs: Insights from Counterintuitive Pseudo-Distributions (2025.naacl-srw)
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Toma Suzuki, Ayuki Katayama, Seiji Gobara, Ryo Tsujimoto, Hibiki Nakatani, Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Recent studies highlight the use of Large Language Models (LLMs) for predicting response distributions as a cost-effective survey method. |
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Challenging the Explanation Based on Preceding Tokens: Discovering Transferable Non-Literal Biasing (2026.acl-short)
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| Challenge: | et al. (2017) show that the generated preceding tokens may push the large language model towards the target answer. |
| Approach: | They find that generated preceding tokens may push large language models towards the target answer . they suggest that the LLM may intentionally use the semantically unrelated tokens to help generation of the target . |
| Outcome: | The generated preceding tokens may push the large language model towards the target answer . the biased connotations of the target response can also transfer to other prompts . |
Prompting is not a substitute for probability measurements in large language models (2023.emnlp-main)
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| Challenge: | Prompting is a dominant method for evaluating the linguistic knowledge of large language models (LLMs). |
| Approach: | They compare metalinguistic prompting and direct probability measurements as ways of measuring LLMs’ linguistic knowledge. |
| Outcome: | The results show that the results relying on metalinguistic prompts cannot be taken as conclusive evidence that an LLM lacks a particular linguistic generalization. |
Understanding the Prompt Sensitivity (2026.acl-long)
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| Challenge: | Prompt sensitivity is a measure of how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt. |
| Approach: | They consider LLMs as multivariate functions and perform a first-order Taylor expansion to analyze the relationship between meaning-preserving prompts, their gradients, and log probabilities of the model’s next token. |
| Outcome: | The proposed model disperses meaning-preserving inputs, making it difficult to reduce to 0. The proposed models also dispersing prompt variants are more likely to introduce prompt sensitivity risks in LLMs. |