Papers with linear regression
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (P18-1)
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| Challenge: | Existing word2vec-based methods for learning rare or unseen words have been criticized for degrading performance in small corpus settings. |
| Approach: | They propose a la carte embedding method that relies on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. |
| Outcome: | The proposed method is based on a new dataset showing that it can be used when a word is encountered even if only a single usage example is available. |
Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers (2020.lrec-1)
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| Challenge: | Using linear regression, we developed dialogue evaluation functions based on features from the WOz dialogues and the MTurkers’ ratings, and the dialogues on the woz participants’ ratings. |
| Approach: | They collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things domain and asked users to rate the system on intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. |
| Outcome: | The proposed evaluation functions can predict three conversational aspects for the Wizard of Oz (WOz) setting. |
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models (2025.naacl-long)
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Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). |
| Approach: | They propose to use a well-established method for text generation to extract token embeddings from multiple layers of LLMs and compute MD scores for each token. |
| Outcome: | The proposed method improves on existing methods and provides accurate and computationally efficient uncertainty scores for sequence-level selective generation and claim-level fact-checking tasks. |
Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)
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| Challenge: | Personal experiences and stories are important in argumentation, but they are not considered in the social sciences. |
| Approach: | They propose to use annotated documents to scale-up the analysis using existing annotations. |
| Outcome: | The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains. |
Quantifying the Persona Effect in LLM Simulations (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. |
| Approach: | They investigate how integrating persona variables—demographic, social, and behavioral factors—impacts LLMs’ ability to simulate diverse perspectives. |
| Outcome: | The proposed model improves on a zero-shot model with persona prompting. |
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary? (2025.coling-main)
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Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro Ozaki, Maria Angelica Riera Machin, Hongyu Sun, Justin Vasselli, Taro Watanabe
| Challenge: | Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words . a recent study shows that frequency from YouTube subtitles is comparable to and often better than the best available resources. |
| Approach: | They use YouTube subtitles to construct frequency norms for five languages . they find they are comparable to and often better than the best currently available resources . |
| Outcome: | The proposed method improves on the best currently available resources for Chinese, English, Indonesian, Japanese, and Spanish. |
Exploring Semantic Capacity of Terms (2020.emnlp-main)
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| Challenge: | Existing models that measure semantic capacity of terms are not all considered equal . a good command of semantic capacity will give us more insight into the granularity of terms . |
| Approach: | They propose a model that evaluates semantic capacity of terms if text corpus can provide enough co-occurrence information of terms. |
| Outcome: | The proposed model can evaluate semantic capacity of terms if the corpus can provide enough co-occurrence information of terms. |