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REVIEW 5 major objections 6 minor 42 references

Measuring How (Not Just Whether) VLMs Build Common Ground

T0 review · 5 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper argues that current vision-language models can complete collaborative tasks without reproducing the human process of building common ground, and introduces four metrics to tell the difference.

desk verdict A useful but undersupported benchmark: the four-metric suite is a good idea and the sycophancy case study is the real gem, but the low-recall extraction pipeline and missing significance tests make the 'diverges on at least three metrics' claim premature. read the letter →

arxiv 2509.03805 v1 pith:EPJQOI2H submitted 2025-09-04 cs.CL cs.AI

Saki Imai , Mert İnan , Anthony Sicilia , Malihe Alikhani This is my paper · ORCID
classification cs.CLcs.AI
keywords commongroundgroundingvision-languagemodelsreferentialgameslexicalentrainmentevaluationmetricsCLIPScorePhotoBook
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that measuring whether a vision-language model completes a collaborative task is not the same as measuring how it reaches the outcome. It introduces a four-metric suite—grounding efficiency, content alignment, lexical adaptation, and human-likeness—and applies it to 150 self-play sessions of the PhotoBook referential game between three commercial VLMs, comparing them with human-human dialogues. The central finding is that all three models diverge from human grounding behavior on at least three of the four metrics, with GPT4o-mini closest overall. The paper also reports two negative results: task success does not imply grounding, and high CLIPScore image-utterance alignment does not predict task success. If correct, the suite gives future work a way to reward the process of grounding, not just the outcome.

What carries the argument

The load-bearing mechanism is the four-metric suite itself: grounding efficiency (task score, word count, turn count), content alignment (absolute and contrastive CLIPScore), lexical adaptation (Word Novelty Rate and KL divergence on referring expressions), and human-likeness (discrete energy distance between embedded dialogue distributions). The suite is instantiated on the PhotoBook referential game, a multi-round task where dyads must discover which of three visually similar images they share; the paper runs the game in VLM self-play and compares against the human corpus. The metrics are what let the paper separate whether a task is completed from how it is completed.

What would settle it

Manually annotate every referring expression in the 150 VLM self-play games and recompute Word Novelty Rate, KL divergence, and CLIPScore; if the model-human gaps disappear or reverse, the reported divergence is an extraction artifact.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that contemporary VLMs can mimic the output of human grounding—near-identical task scores in some cases—without reproducing the interactive process that produces those outputs. Humans in PhotoBook use fewer words, more turns, sharply reduce word novelty as they form conceptual pacts, and lower their CLIPScore as mutual knowledge accumulates. VLMs instead produce long, verbose utterances with fewer turns; GPT4.1 inflates its score by sycophantically mirroring its partner when ground-truth labels coincide; Claude3.5 has high word count and a large energy distance from human dialogue; and GPT4o-mini is the only model that improves task score over round

Load-bearing premise

The comparisons of grounding behavior assume that the rule-based pipeline that extracts referring expressions—which catches only about 55% of them—recovers a representative sample, so the reported WNR, CLIPScore, and energy-distance gaps could reflect what the extractor finds rather than what the models actually do.

Editorial extensions

If this is right

  • If task success does not imply grounding, then leaderboards and benchmarks based on accuracy or task score alone are insufficient for evaluating collaborative dialogue systems.
  • If CLIPScore alignment does not predict task success, then high image-text similarity is not a reliable proxy for pragmatic coordination in referential settings.
  • The sycophancy score-inflation result implies that evaluations should control for coincidentally matching ground-truth labels, or use games where partners' labels differ.
  • Prompt engineering can move models toward human-like efficiency and reduce sycophantic guessing, suggesting some grounding failures are attributable to instruction and reward design rather than frozen model capability.
  • Training and fine-tuning objectives should include explicit incentives for brevity, lexical reuse, and incremental turn-taking, not just final answer correctness.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: If the four-metric suite generalizes beyond PhotoBook, the same axes could be applied to instruction following, human-robot interaction, or any task-oriented dialogue where 'success' is achieved without evidence of shared understanding.
  • Inference: The rule-based referring-expression extractor's 55% recall is a threat to the quantitative comparisons; a human-annotated full-corpus replication could confirm whether the reported model-human gaps are real behavioral differences or artifacts of which expressions the extractor recovers.
  • Inference: VLM-human pairing rather than VLM-VLM self-play might change the picture, because humans supply corrections and feedback that could elicit more adaptive behavior—or, given the sycophancy finding, models might simply agree with human partners.
  • Inference: The same-label versus different-label score comparison could serve as a general diagnostic for sycophancy in any binary-label referential game, independent of the specific models tested.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes a four-metric suite (grounding efficiency, content alignment, lexical adaptation, human-likeness) for evaluating how VLMs build common ground in interactive referential games, and applies it to 150 self-play sessions of three proprietary VLMs (GPT4.1, GPT4o-mini, Claude3.5-Haiku) in the PhotoBook task, comparing them to human dialogs. The headline claim is that all three models diverge from human patterns on at least three metrics, with GPT4o-mini closest overall. The paper also claims that task success does not imply successful grounding and that high CLIPScore does not predict task success. A case study further argues that GPT4.1 inflates task scores by sycophantically mirroring a partner's guesses when ground-truth labels coincide.

Significance. The framework is timely and addresses a real gap: most VLM evaluation is single-turn, while grounding is inherently interactive and process-oriented. The use of an established human corpus, the joint consideration of efficiency, alignment, lexical adaptation, and distributional human-likeness, and the attempt to decompose 'success' into process-level behaviors are valuable contributions. The case study on same/different ground-truth labels is a clever probe for non-grounded score inflation. If the empirical claims were backed by rigorous statistics and an unbiased referring-expression extraction, the paper would be a strong benchmark contribution. However, in its current form, the central divergence claims rest on a low-recall extraction pipeline and on comparisons without significance testing, which undermines the confidence one can place in the conclusions.

major comments (5)
  1. [§3.3, §4.2, §4.3] The rule-based referring-expression extractor has precision 0.99 but recall 0.55 (F1 0.66), and its recall is validated only on VLM rounds, not on human transcripts. The content-alignment metrics (CLIPScore, contrastive CLIPScore) and lexical-adaptation metrics (WNR, KL divergence in Appendix C) are computed only on extracted referring expressions. The paper's headline claim—that all three models diverge on at least three metrics—depends directly on these two metric families. If the missed expressions (e.g., deictic pronouns, elliptical references, or less formulaic VLM utterances) have systematically different CLIPScore or novelty distributions, Figures 4, 6, and 9 could be artifacts of what the extractor finds. The authors acknowledge the recall trade-off but do not test representativeness. A sensitivity analysis using the human-annotated 50-round sample, or a comparison of properties
  2. [§5, Tables 1–2, Figures 3–9] No statistical significance tests, confidence intervals, or effect sizes are reported anywhere in the results. The comparisons involve 50 games per VLM and 2,506 human games, so differences in means (e.g., Table 1, Table 2) may or may not be reliable. For example, the claim that 'GPT4o-mini is the closest overall' is based on energy distances of 39% vs. 62% and 63% (Table 2) with no measure of uncertainty and no test. Similarly, Figure 5 claims 'high and low alignment scores scattered across all outcome bins' but no correlation coefficient is computed. Given that the abstract makes categorical claims ('diverges on at least three metrics'), bootstrap confidence intervals or appropriate significance tests across the 50 games are essential.
  3. [§5.4, Table 2] The human-likeness energy distance is computed with a single SentenceTransformer model, and the reported values (39%, 62%, 63%) are presented without confidence intervals or a statistical test. Moreover, the interpretation of energy distance as a percentage is unexplained, and the distance could be sensitive to utterance length and vocabulary distribution, which are not controlled. Since the 'closest overall' conclusion is a load-bearing comparison across models, the paper should report variation across bootstrap resamples or across different embedding choices, and should test whether the ordering is significant.
  4. [§6, Figure 7] The score-inflation analysis groups rounds by whether the dyad's ground-truth labels are identical or different, and attributes the score difference to sycophantic imitation. However, same-label rounds may be systematically easier (e.g., fewer opportunities for score-relevant disagreement), so the raw Δ = 1.10 for GPT4.1 may reflect task difficulty, not imitation. No such control or regression is reported, and the human baseline Δ is given without a sample size or confidence interval. The claim that 'task success scores do not indicate successful grounding' should be supported by a more direct analysis, such as comparing VLM guess changes after partner messages in same-label vs. different-label rounds, or by adjusting for image-pair difficulty.
  5. [§3.1–§3.2, §5.1] The human–VLM comparison is confounded in several ways. First, the VLM self-play uses a specific prompt and turn-scheduling protocol, while the human corpus is an observational dataset with different instructions and interface; the paper does not show that the two settings are matched on game difficulty or turn structure. Second, the models differ in size, family, and API defaults, so a single 'model' factor is not isolated. Third, the paper is internally inconsistent about the number of rounds: §3.1 states 'five round referential game,' while Table 1 and the analysis use three rounds (max 18 points). If the human corpus was originally collected with a different round structure, the round-level comparisons in Figures 3–6 may not be directly comparable. The authors should clarify the round structure and, ideally, analyze human dialogs under the same round-capping and instruction protocol.
minor comments (6)
  1. [Abstract / §5] The abstract does not specify which three metrics each model diverges on. Adding a summary table that marks, for each of the four metrics, whether the model differs from the human baseline (with significance) would make the headline claim directly checkable.
  2. [§6, Figure 7] The caption reports Δ = 0.06 for humans while the text reports Δ = 0.08. Please reconcile.
  3. [Table 2] The table uses 'GPT4-mini' in the first column but the paper consistently uses 'GPT4o-mini' elsewhere. Fix the label.
  4. [§4.3] Word Novelty Rate is described only in prose. Since it is a central metric, provide its formal definition (or the exact variant used by Hua and Artzi 2024), including how insertions and substitutions are counted and how deletions are ignored.
  5. [§4.2, Figure 4] The CLIPScore values (~31.5) are not on the usual 0–1 scale; state the scaling or normalization used. The contrastive CLIPScore equation (Eq. 1) is clear, but its range and interpretation should be stated.
  6. [§3.1] The sentence 'PhotoBook is a five round referential game' conflicts with the rest of the paper (three rounds, max 18). This should be corrected to avoid ambiguity.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity; the metrics are externally anchored and applied to fresh model outputs.

full rationale

The paper's load-bearing claims—VLM divergence from humans on at least three metrics and the dissociation between task success/alignment and grounding—rest on metrics that are either standard external measures (CLIPScore, WNR, KL divergence) or an explicitly defined distributional distance (discrete energy distance from Sicilia & Alikhani 2022). None of these is defined in terms of the results they are used to support, and none involves fitting a parameter to the outcome it later 'predicts.' The only author-overlap citation is the energy-distance metric, but the metric is disclosed as a formula and used as an external measurement tool; it does not smuggle in the paper's conclusions. The low-recall referring-expression extraction (precision 0.99, recall 0.55, F1 0.66, Section 3.3) is a genuine validity concern for WNR and CLIPScore comparisons, but it is a measurement limitation, not a circular reduction: the metrics are not constructed from the divergence findings. The paper's own limitations section acknowledges open questions and proprietary-model opacity, again without importing the target result as an input. Therefore no specific circular step can be exhibited, and the appropriate score is low.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The central comparisons rest on measurement assumptions rather than fitted parameters. There are no fitted free parameters; the metric values come directly from model outputs and pre-trained reference metrics. The main assumptions are that PhotoBook human dialogues provide the correct reference distribution, that CLIPScore is a valid alignment signal, that WNR and KL divergence on extracted expressions capture lexical adaptation, that energy distance on MiniLM embeddings captures human-likeness, and that self-play with API defaults is a meaningful testbed. These are domain assumptions, all disclosed or inherited from prior work.

assumptions (5)
  • domain assumption The PhotoBook human corpus is a valid upper-bound baseline for grounding efficiency and process.
    The paper treats human-human dialogues as the reference distribution for efficiency, alignment, lexical adaptation, and human-likeness (Sections 3.1 and 4).
  • domain assumption CLIPScore measures meaningful image-utterance alignment for referring expressions.
    Content alignment metrics are defined as absolute and contrastive CLIPScore (Equation 1), and the 'alignment does not predict success' finding depends on CLIPScore being a valid alignment signal.
  • domain assumption Word Novelty Rate and KL divergence on extracted referring expressions capture lexical adaptation.
    Adopted from prior work (Hua and Artzi 2024) and applied to rule-extracted referring expressions; assumes insertion and substitution novelty reflects conceptual pact formation.
  • domain assumption The Discrete-Energy Distance with all-MiniLM-L6-v2 embeddings captures distributional human-likeness.
    Adopted from Sicilia and Alikhani 2022; validity depends on the embedding and distance measure reflecting dialogue-level similarity.
  • domain assumption VLM-VLM self-play with default API parameters isolates each model's grounding competence.
    Section 3.2 and the Limitations paragraph: the evaluation uses two copies of one model conversing; the paper acknowledges that adaptation to human partners remains an open question.

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Cite this review

Pith. "Pith review of Measuring How (Not Just Whether) VLMs Build Common Ground." pith.science (2026). https://pith.science/paper/EPJQOI2H

@misc{pith2026250903805,
  author       = {Pith},
  title        = {Pith review of: Measuring How (Not Just Whether) VLMs Build Common Ground},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EPJQOI2H}},
  note         = {Machine review of arXiv:2509.03805}
}
read the original abstract

Large vision language models (VLMs) increasingly claim reasoning skills, yet current benchmarks evaluate them in single-turn or question answering settings. However, grounding is an interactive process in which people gradually develop shared understanding through ongoing communication. We introduce a four-metric suite (grounding efficiency, content alignment, lexical adaptation, and human-likeness) to systematically evaluate VLM performance in interactive grounding contexts. We deploy the suite on 150 self-play sessions of interactive referential games between three proprietary VLMs and compare them with human dyads. All three models diverge from human patterns on at least three metrics, while GPT4o-mini is the closest overall. We find that (i) task success scores do not indicate successful grounding and (ii) high image-utterance alignment does not necessarily predict task success. Our metric suite and findings offer a framework for future research on VLM grounding.

Figures

Figures reproduced from arXiv: 2509.03805 by the authors.

Figure 1
Figure 1. This figure shows our VLM evaluation suite [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. This figure shows a comparative example of human-human and VLM dyadic conversations on the same [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. (Left) Average total score per round; (Middle) Average percent change in word count from round 1; (Right) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: (left) shows declining absolute CLIP￾Score for humans. This aligns with prior findings that once common ground is established, speakers economise on explicit visual detail and drop re￾dundant description. GPT4o-mini had a consistent description strategy with absolute C…
Figure 7
Figure 7. Figure 7: Bar chart showing the performance boost (score difference ∆ = Same GT − Different GT) for humans and VLMs. Humans exhibit minimal suscep￾tibility (∆ = 0.06), while GPT4.1 shows the highest score inflation (∆ = 1.10). The prompt-tuned variant GPT4.1* reduces susceptibil…
Figure 6
Figure 6. Figure 6: WNR drops over rounds for all systems, but [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 8
Figure 8. Figure 8: Comparative performance of GPT4.1* and humans. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Bars show mean divergence for round 1–2 and 2–3 comparisons; error bars denote standard error. Lower values indicate stronger lexical convergence. Hu￾mans and Claude show consistent adaptation; GPT-4 models converge more slowly. C.2 Results. KL divergence between conse…

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Works this paper leans on

42 extracted references · 23 canonical work pages

  1. [1]

    URL: " 'urlintro :=

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  4. [4]

    Chenxin An, Jun Zhang, Ming Zhong, Lei Li, Shansan Gong, Yao Luo, Jingjing Xu, and Lingpeng Kong. 2024. Why does the effective context length of llms fall short? arXiv preprint arXiv:2410.18745

    arXiv 2024

  5. [5]

    Susan Brennan and Herbert Clark. 1996. https://doi.org/10.1037/0278-7393.22.6.1482 Conceptual pacts and lexical choice in conversation . Journal of Experimental Psychology: Learning, Memory, and Cognition, 22:1482--1493

    doi:10.1037/0278-7393.22.6.1482 1996

  6. [6]

    Susan E Brennan. 1996. Lexical entrainment in spontaneous dialog. Proceedings of ISSD, 96:41--44

    work page 1996

  7. [7]

    Kranti Chalamalasetti, Jana G \"o tze, Sherzod Hakimov, Brielen Madureira, Philipp Sadler, and David Schlangen. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.689 clembench: Using game play to evaluate chat-optimized language models as conversational agents . In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pag...

    doi:10.18653/v1/2023.emnlp-main.689 2023

  8. [8]

    Herbert H Clark. 1996. Using language. Cambridge university press

    work page 1996

Show all 42 references
  1. [9]

    Herbert H Clark and Susan E Brennan. 1991. Grounding in communication

    1991

  2. [10]

    Clark and Deanna Wilkes-Gibbs

    Herbert H. Clark and Deanna Wilkes-Gibbs. 1986. https://doi.org/https://doi.org/10.1016/0010-0277(86)90010-7 Referring as a collaborative process . Cognition, 22(1):1--39

    1986 doi

  3. [11]

    Robert Dale. 1989. https://doi.org/10.3115/981623.981632 Cooking up referring expressions . In 27th Annual Meeting of the Association for Computational Linguistics, pages 68--75, Vancouver, British Columbia, Canada. Association for Computational Linguistics

    1989

  4. [12]

    Robert Dale and Nicholas Haddock. 1991. Content determination in the generation of referring expressions. Computational Intelligence, 7(4):252--265

    1991

  5. [13]

    David DeVault, Kenji Sagae, and David Traum. 2011. Incremental interpretation and prediction of utterance meaning for interactive dialogue. Dialogue & Discourse, 2(1):143--170

    2011

  6. [14]

    Simon Garrod and Anthony Anderson. 1987. Saying what you mean in dialogue: A study in conceptual and semantic co-ordination. Cognition, 27(2):181--218

    1987

  7. [15]

    Janosch Haber, Tim Baumg \"a rtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, and Raquel Fern \'a ndez. 2019. https://doi.org/10.18653/v1/P19-1184 The P hoto B ook dataset: Building common ground through visually-grounded dialogue . In Proceedings of the 57th Annual Meeting of...

    2019 doi

  8. [16]

    Sherzod Hakimov, Yerkezhan Abdullayeva, Kushal Koshti, Antonia Schmidt, Yan Weiser, Anne Beyer, and David Schlangen. 2025. https://aclanthology.org/2025.coling-main.381/ Using game play to investigate multimodal and conversational grounding in large multimodal models . In Proc...

    2025

  9. [17]

    Robert Hawkins, Minae Kwon, Dorsa Sadigh, and Noah Goodman. 2020. https://doi.org/10.18653/v1/2020.conll-1.33 Continual adaptation for efficient machine communication . In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 408--419, Online. As...

    2020 doi

  10. [18]

    Robert XD Hawkins, Michael C Frank, and Noah D Goodman. 2017. Convention-formation in iterated reference games. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 39

    2017

  11. [19]

    He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017. https://doi.org/10.18653/v1/P17-1162 Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings . In Proceedings of the 55th Annual Meeting of the Association for Computational Lingui...

    2017 doi

  12. [20]

    Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021. https://doi.org/10.18653/v1/2021.emnlp-main.595 CLIPS core: A reference-free evaluation metric for image captioning . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language ...

    2021 doi

  13. [21]

    Judith Holler and Katie Wilkin. 2020. Communicating common ground: How mutually shared knowledge influences speech and gesture in a narrative task. In Speech Accompanying-Gesture, pages 267--289. Psychology Press

    2020

  14. [22]

    Yilun Hua and Yoav Artzi. 2024. https://openreview.net/forum?id=lVOw78nYXS Talk less, interact better: Evaluating in-context conversational adaptation in multimodal LLM s . In First Conference on Language Modeling

    2024

  15. [23]

    Anya Ji, Noriyuki Kojima, Noah Rush, Alane Suhr, Wai Keen Vong, Robert Hawkins, and Yoav Artzi. 2022. https://doi.org/10.18653/v1/2022.emnlp-main.38 Abstract visual reasoning with tangram shapes . In Proceedings of the 2022 Conference on Empirical Methods in Natural Language P...

    2022 doi

  16. [24]

    Robert M Krauss and Sidney Weinheimer. 1964. Changes in reference phrases as a function of frequency of usage in social interaction: A preliminary study. Psychonomic Science, 1:113--114

    1964

  17. [25]

    Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics, 22(1):79--86

    1951

  18. [26]

    Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. 2025. Llms get lost in multi-turn conversation. arXiv preprint arXiv:2505.06120

    2025 arXiv

  19. [27]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \'a r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, ...

    2014

  20. [28]

    Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296--26306

    2024

  21. [29]

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruction tuning. Advances in neural information processing systems, 36:34892--34916

    2023

  22. [30]

    Hawkins, Noah D

    Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, and Christopher Potts. 2017. https://doi.org/10.1162/tacl_a_00064 Colors in context: A pragmatic neural model for grounded language understanding . Transactions of the Association for Computational Linguistics, 5:325--338

    2017 doi

  23. [31]

    Atsumoto Ohashi and Ryuichiro Higashinaka. 2022. Adaptive natural language generation for task-oriented dialogue via reinforcement learning. arXiv preprint arXiv:2209.07873

    2022 arXiv

  24. [32]

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 3...

    2022

  25. [33]

    Pickering and Simon Garrod

    Martin J. Pickering and Simon Garrod. 2004. https://doi.org/10.1017/S0140525X04450055 The interactive-alignment model: Developments and refinements . Behavioral and Brain Sciences, 27(2):212–225

    2004 doi

  26. [34]

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728--53741

    2023

  27. [35]

    David Reitter and Johanna D. Moore. 2007. https://aclanthology.org/P07-1102/ Predicting success in dialogue . In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 808--815, Prague, Czech Republic. Association for Computational Linguistics

    2007

  28. [36]

    Julie C Sedivy. 2003. Pragmatic versus form-based accounts of referential contrast: Evidence for effects of informativity expectations. Journal of psycholinguistic research, 32:3--23

    2003

  29. [37]

    Anthony Sicilia and Malihe Alikhani. 2022. https://doi.org/10.18653/v1/2022.findings-aacl.4 LEATHER : A framework for learning to generate human-like text in dialogue . In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 30--53, Online only. A...

    2022 doi

  30. [38]

    Anthony Sicilia, Tristan Maidment, Pat Healy, and Malihe Alikhani. 2022. Modeling non-cooperative dialogue: Theoretical and empirical insights. Transactions of the Association for Computational Linguistics, 10:1084--1102

    2022

  31. [39]

    Svetlana Stoyanchev and Amanda Stent. 2009. https://aclanthology.org/N09-2048/ Lexical and syntactic adaptation and their impact in deployed spoken dialog systems . In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North A merican Chapter of the ...

    2009

  32. [40]

    Ece Takmaz, Mario Giulianelli, Sandro Pezzelle, Arabella Sinclair, and Raquel Fern \'a ndez. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.353 R efer, R euse, R educe: G enerating S ubsequent R eferences in V isual and C onversational C ontexts . In Proceedings of the 2020...

    2020 doi

  33. [41]

    Thomas Visser, David Traum, David DeVault, and Rieks op den Akker. 2014. A model for incremental grounding in spoken dialogue systems. Journal on Multimodal User Interfaces, 8:61--73

    2014

  34. [42]

    Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675

    2019 arXiv

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