Papers with theoretical frameworks

8 papers
A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models (2024.lrec-main)

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Challenge: Large Language Models (LLMs) have been developed without a theoretical framework . evaluating and improving LLMs will benefit from theoretical frameworks that enable comparison of structures of human language and model of language built up by LLM.
Approach: They propose to use a construction grammar schema corpus to compare human grammar to LLMs' model of language.
Outcome: The proposed corpus shows that even the largest LLMs are limited to more substantive constructions and do not recognize similarity of purely schematic constructions.
Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth (2026.eacl-long)

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Challenge: despite advances in transformers, their theoretical limitations in discrete reasoning remain a critical open problem.
Approach: They synthesize recent advances from three theoretical perspectives to clarify structural and computational barriers transformers face when performing symbolic computations.
Outcome: The proposed models excel at pattern matching and interpolation, but they face bottlenecks in communication and depth constraints.
All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for German (2020.lrec-1)

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Challenge: Using the GerCo dataset, we identify adjective-noun collocations in German and compare them with statistical associations measures.
Approach: They present a GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’.
Outcome: The GerCo dataset contains 4,732 positive and negative instances of collocations and covers all 16 semantic classes of adjectives defined in the German wordnet GermaNet.
Mitigating Sequential Dependencies: A Survey of Algorithms and Systems for Generation-Refinement Frameworks in Autoregressive Models (2025.findings-emnlp)

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Challenge: Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models .
Approach: They analyze methods based on generation strategies and refinement mechanisms . they examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation.
Outcome: The proposed frameworks can be used to improve the quality of autoregressive models.
In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)

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Challenge: Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue.
Approach: They propose to annotate a corpus of 33 dialogues and over 9,000 utterance units using the Dependency Dialogue Acts framework.
Outcome: The proposed corpus spans four genres of multi-party conversations from different modalities.
Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)

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Challenge: Discourse analysis is a hard task, but data is limited for other languages.
Approach: They propose to use zero-shot learning to combine discourse relation data . they compare two versions of the same text with different labels .
Outcome: The proposed method can be applied to languages, frameworks, or similarity measures.
LLM Beliefs Are in Their Heads (2026.acl-long)

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Challenge: Using linear controlled probes, we investigate belief-like representations in decoder-only autoregressive LLMs using residual stream activations and single attention heads.
Approach: They develop four different experiments on decoder-only autoregressive LLMs and examine how they fare against these standards.
Outcome: The proposed representations exhibit strong truth sensitivity and consistent accuracy across models and data sets.
From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models (2026.findings-acl)

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Challenge: Large Language Models (LLMs) have remarkable capabilities, but unreliability remains a barrier to deployment in high-stakes domains.
Approach: They propose to transform uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.
Outcome: The proposed model evolution from passive diagnostic metric to active control signal is critical for high-stakes applications.

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