ANAPHORA RESOLUTION: THE STATE OF THE ART

Paper
LLM Failure ModesNLP and Linguistics

The paper is an introduction to anaphora resolution offering a brief survey of the major works in the field.

Introduction. Anaphora resolution is a complicated problem in Natural Language Processing and has attracted the attention of many researchers. The approaches developed - traditional (from purely syntactic ones to highly semantic and pragmatic ones), alternative (statistic, uncertainty-reasoning etc.) or knowledge-poor, offer only approximate solutions. The etymology of the term "anaphora" goes back to Ancient Greek with “anaphora” (αναφορα) being a compound word consisting of the separate words ανα − back, upstream, back in an upward direction and φορα - the act of carrying and denoted the act of carrying back upstream. For Computational Linguists embarking upon research in the field of anaphor resolution, I strongly recommend as a primer Graham Hirst's book "Anaphora in natural language understanding" (Hirst 1981) which may seem a bit dated in that it does not include developments in the 80's and the 90's, but which provides an excellent survey of the theoretical work on anaphora and of the early computational approaches and is still very useful reading.

Discussion / Conclusion. Against the background of growing interest in the field, it seems that insufficient attention has been paid to the evaluation of the systems developed. Even though the number of works reporting extensively on evaluation in anaphora resolution is increasing (Aone & Bennet 1996; Azzam et al. 1998; Baldwin 1997; Gaizauskas & Humphreys 1996; Lappin & Leass 1994, Mitkov & Stys 1997, Mitkov et al. 1998), the forms of evaluation that have been proposed are not sufficient or perspicuous. It is felt, however, that evaluation in anaphora resolution needs further attention. Measuring the success rate of an anaphora resolution system in terms of "recall" and "precision" is undoubtedly an important (and consistent) step in assessing the efficiency of anaphora resolution approaches, but as we have already pointed out, they cannot be seen as distinct measures for robust systems. In addition, it appears that they alone 10The above definitions refer to the resolution process only and not to the process of identification of anaphors which of course will have "recall" and "process" defined differently. Automatic referential links in corpora is a highly attractive research task that will definitely need further attention in the future.

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Do single-axis benchmarks accurately measure agent capability for real deployment? What prevents LLMs from applying their reasoning knowledge to improve outputs? What makes reasoning traces effective supervision even when they're incorrect? Can mechanistic interpretability methods reliably reveal what models actually know? How does decomposing tasks into separate stages affect reasoning quality and safety? How do thinking tokens exhibit diminishing returns in reasoning? Do language models encode knowledge that influences generation, or primarily imitate surface patterns? What prevents language models from performing systematic logical reasoning? Can minimal training unlock latent reasoning already present in base models? Can reasoning traces reveal actual model reasoning versus plausible output? How does fine-tuning trade off accuracy against reasoning quality? How does diversity prevent model convergence on superficial patterns? Why does self-revision amplify confidence in wrong model answers? How does policy entropy collapse limit scaling of reasoning-focused reinforcement learning? Why do multi-agent systems reach premature consensus without genuine deliberation? How does model capacity affect learning performance on diverse downstream tasks? Can language models reason beyond surface pattern matching?