Papers with unsupervised framework
Fine-Tuning Language Models with Reward Learning on Policy (2024.naacl-long)
Copied to clipboard
| Challenge: | Reinforcement learning from human feedback (RLHF) is an effective approach to align large language models (LLMs) to human preferences. |
| Approach: | They propose a framework that refines a reward model using policy samples to keep it on-distribution. |
| Outcome: | The proposed framework outperforms the state-of-the-art on three benchmark datasets showing that it can learn robust representations of policy samples. |
An unsupervised framework for tracing textual sources of moral change (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on moral sentiment classification and temporal inference of moral sentiment have not quantified the origins of these changes. |
| Approach: | They propose an unsupervised framework for tracing textual sources of moral change toward entities through time. |
| Outcome: | The proposed framework captures fine-grained human moral judgments and identifies coherent source topics of moral change triggered by historical events. |
Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment (2025.emnlp-industry)
Copied to clipboard
Shramona Chakraborty, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Ronen Kat, Itay Etelis, Mohamed Mahameed, Itai Guez, Rachel Tzoref-Brill
| Challenge: | a framework to align attributes that refer to the same concept but differ across schemas is challenging in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and domain-specific terminologies. |
| Approach: | They propose a framework that combines contextual reasoning and embedding-based similarity to address token limitations and hallucinations. |
| Outcome: | The proposed framework scales to large schemas and shows strong performance on healthcare schemas. |
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. |
| Approach: | They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. |
| Outcome: | The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches. |
Scientific Paper Extractive Summarization Enhanced by Citation Graphs (2022.emnlp-main)
Copied to clipboard
| Challenge: | citation graphs can be used to extract scientific papers under different conditions. |
| Approach: | They propose a multi-granularity unsupervised summarization model that fine tunes a pre-trained encoder model on the citation graph by link prediction tasks. |
| Outcome: | The proposed model outperforms baseline models on a public benchmark dataset. |
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)
Copied to clipboard
Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources. |
| Approach: | They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets. |
| Outcome: | The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources. |
Unsupervised Commonsense Question Answering with Self-Talk (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current systems rely on pre-trained language models or external knowledge bases to incorporate additional relevant knowledge. |
| Approach: | They propose an unsupervised framework based on self-talk to improve commonsense performance by asking language models to ask information seeking questions. |
| Outcome: | Empirical results show that the proposed framework improves on four out of six commonsense benchmarks and competes with models that obtain knowledge from external KBs. |
Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on event extraction depend on pre-defined argument roles . despite great progress, many studies still rely on hand-crafted ontologies . |
| Approach: | They propose an unsupervised framework for customizing argument roles for event extraction . they propose a human-annotated event extraction dataset with 143 customized argument roles . |
| Outcome: | The proposed framework outperforms existing methods on an event extraction dataset. |
Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
| Approach: | They propose an unsupervised framework that generates human-like multi-hop training data from homogeneous and heterogeneously data sources. |
| Outcome: | The proposed framework achieves 61% and 83% of the supervised learning performance for the HybridQA and HotpotQA datasets. |
Neutralizing Bias in LLM Reasoning using Entailment Graphs (2025.findings-acl)
Copied to clipboard
| Challenge: | Natural Language Inference (NLI) is a foundational understanding task in language understanding. |
| Approach: | They propose a framework to construct counterfactual reasoning data and fine-tune LLMs to reduce attestation bias. |
| Outcome: | The proposed framework reduces hallucinations from attestation bias on original and bias-neutralized datasets while keeping hypotheses unchanged. |
An Unsupervised, Geometric and Syntax-aware Quantification of Polysemy (2022.emnlp-main)
Copied to clipboard
| Challenge: | Polysemy is the phenomenon where a single word form possesses two or more related senses. |
| Approach: | They propose an unsupervised framework to quantify polysemy for words in multiple languages . they use syntactic knowledge to infuse the framework with syntaktic knowledge . |
| Outcome: | The proposed framework is based on syntactic knowledge and is compared with existing methods in English, French and Spanish. |
Community-Cross-Instruct: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities (2024.emnlp-main)
Copied to clipboard
| Challenge: | Social scientists use surveys to learn opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. |
| Approach: | They propose a framework for aligning large language models to online communities by finetuning instruction-output pairs by an advanced LLM to elicit their beliefs. |
| Outcome: | The proposed framework enables cost-effective and automated surveying of diverse online communities. |
Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events (2025.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art automatic event detection struggles with interpretability and adaptability to evolving large-scale key events. |
| Approach: | They propose a task which identifies episodes within a news corpus of key event articles. |
| Outcome: | The proposed framework achieves 59.2% gain across all metrics compared to baselines. |
Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization (2026.acl-long)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompts elicit multi-step reasoning, yet how reasoning related structure is expressed during training remains poorly understood. |
| Approach: | They propose a framework that tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities. |
| Outcome: | The proposed framework tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities. |
SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping (2026.acl-long)
Copied to clipboard
| Challenge: | Existing embedding models rely on implicit, imprecise and fixed notion of similarity to evaluate scientific abstracts. |
| Approach: | They propose a framework for generating multifaceted embeddings of scientific abstracts . they propose an unsupervised procedure that produces aspect-specific summarizing sentences . |
| Outcome: | The proposed framework captures distinct, individually specifiable aspects in isolation . it then trains embedding models to map semantically related summaries to nearby positions . the proposed framework is evaluated in the domains of invasion biology and medicine . |