Papers with CLEAR
CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)
Copied to clipboard
| Challenge: | Existing multilingual embedding models often struggle to capture cross-lingual alignment during training. |
| Approach: | They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English. |
| Outcome: | The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English. |
Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents (2026.acl-demo)
Copied to clipboard
| Challenge: | Agentic systems are becoming more capable of defining strategies, taking actions, and solving complex, multi-step tasks. |
| Approach: | They propose an automatic, dynamic, and easy-to-use evaluation framework that provides textual insights into agent behavior on three levels of granularity: system, trace, and node. |
| Outcome: | The proposed framework produces high-quality, data-driven, insightful feedback on system, trace, and node. |
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |
Multilingual and Cross-Lingual Graded Lexical Entailment (P19-1)
Copied to clipboard
| Challenge: | a novel method for capturing graded (and binary) LE is developed for cross-lingual generalisation of lexical entailment . lexicale enlargement is a key principle behind hierarchical structure found in semantic networks . |
| Approach: | They propose a method for cross-lingual generalisation of GR-LE relation using hyperlex and a bilingual dictionary. |
| Outcome: | The proposed method outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin. |
CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation (2025.findings-emnlp)
Copied to clipboard
Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li, Zecong Tang, Benjamin M. Mervak, Lydia Chelala, Christopher M Straus, Reve Chahine, Samuel G. Armato Iii, Chenhao Tan
| Challenge: | Existing metrics lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports. |
| Approach: | They propose a tabular framework with E**xpert-curated labels and an attribute-level comparison for radiology report evaluation (**CLEAR) |
| Outcome: | The proposed framework can extract clinical attributes and provide automated metrics that are strongly aligned with clinical judgment. |
Motivational Interviewing Transcripts Annotated with Global Scores (2024.lrec-main)
Copied to clipboard
| Challenge: | Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change. |
| Approach: | They propose to annotate MI therapy sessions written in English from public sources . they explore the potential use of the dataset for training MI language models . |
| Outcome: | The proposed dataset includes 242 MI demonstration transcripts annotated with therapist behavioral codes and global scores and client language EAsy Rating (CLEAR) tags for client speech. |
CLEAR: Character Unlearning in Textual and Visual Modalities (2025.findings-acl)
Copied to clipboard
Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin, Boris Mikheev, Denis Bobkov, Aibek Alanov, Oleg Rogov, Ivan Oseledets, Elena Tutubalina
| Challenge: | Existing methods for removing private or hazardous data from deep learning models are focused on single-modality models. |
| Approach: | They propose CLEAR, the first open-source benchmark specifically for MMU. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs. |
| Outcome: | The proposed benchmarks show that unlearning both modalities outperform single-modality approaches. |
CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Argument Improvement (ArgImp) is a text rewriting task that requires LLMs to shorten texts while increasing word length and merging sentences. |
| Approach: | They propose to use a pipeline to evaluate LLMs' behavior in a text rewriting setting . they use four linguistic levels to examine the qualities of argumentative texts . |
| Outcome: | The proposed evaluation pipeline compares LLMs on argumentative texts and their improvement on a broad set of argumentation corpora. |