Papers with in-context learning methods
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement (2024.eacl-long)
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| Challenge: | Recent studies have focused on the application and evaluation of Large Language Models (LLMs) but LLMs are still prone to factual errors and inconsistencies in their explanations, offering limited control and interpretability for inference in complex domains. |
| Approach: | They propose an abductive-deductive framework that integrates Large Language Models with an external backward-chaining solver to refine step-wise natural language explanations. |
| Outcome: | The proposed framework improves explanations generated via in-context learning methods and Chain-of-Thought (CoT) on ethical NLI tasks while producing formal proofs describing and supporting models’ reasoning. |
Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls (2026.acl-industry)
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Rasmus T. Aavang, Rasmus Tjalk-Bøggild, Alexandre Iolov, Giovanni Rizzi, Mike Zhang, Johannes Bjerva
| Challenge: | Earnings calls are a key source of financial information about public companies. extracting information from earnings calls is difficult. |
| Approach: | They propose to use LLMs to perform open-ended extraction from unstructured call transcripts to provide a baseline for this valuable domain through the consistent tracking of emergent KPIs. |
| Outcome: | The proposed method provides a baseline for this valuable domain through the consistent tracking of emergent KPIs. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
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| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
CIE: Controlling Language Model Text Generations Using Continuous Signals (2025.emnlp-main)
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| Challenge: | Existing methods to control language models with intent are brittle and hard to scale. |
| Approach: | They propose to use a set of LMs to fine-tune to expect a control vector that is interpolated between a "low" and a 'high' token embedding. |
| Outcome: | The proposed method can be finetuned to expect a control vector that is interpolated between a “low” and a ‘high” token embedding. |
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods (2024.lrec-main)
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Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J. Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, Özlem Uzuner, Meliha Yetisgen
| Challenge: | Social determinants of health (SDoH) are often studied in the electronic health record (EHR) however, there are difficulties in documenting SDoH in a tabular format due to the lack of a comprehensive SDoh tool. |
| Approach: | They propose to annotate social history sections from 1,260 clinical notes from pediatric patients within the University of Washington (UW) hospital system. |
| Outcome: | The proposed corpus captures ten distinct health determinants including living and economic stability, prior trauma, education access, substance use history, and mental health with an overall annotator agreement of 81.9 F1. |
ProcWorld: Benchmarking Large Model Planning in Reachability-Constrained Environments (2025.emnlp-main)
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| Challenge: | Existing benchmarks for embodied spatial reasoning and long-term planning are non-trivial due to the combinatorial complexity of long-horizon abstract reasoning. |
| Approach: | They propose a large-scale benchmark for partially observable embodied spatial reasoning and long-term planning with large language models and vision language models. |
| Outcome: | The proposed model performs better in 16 task types, 5,000 rooms, and over 10 million evaluation trajectories with diverse data distribution. |