CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

Jey Han Lau; Lea Frermann; Ming-Bin Chen

arxiv: 2604.15647 · v2 · submitted 2026-04-17 · 💻 cs.CL

CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

Ming-Bin Chen , Jey Han Lau , Lea Frermann This is my paper

Pith reviewed 2026-05-10 08:58 UTC · model grok-4.3

classification 💻 cs.CL

keywords memorydeliberativeutteranceconversationconversationaldialoguesdimensionsdynamics

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The pith

CIG scores utterances using novelty, relevance, and implication scope derived from a dynamic semantic memory model, outperforming traditional heuristics in correlating with human judgments on deliberative segments.

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

The authors create a system that breaks down conversation utterances into atomic claims and keeps track of these claims in a structured memory that updates over time. Each new utterance is then evaluated based on three aspects: how new the information is, how relevant it is to the topic, and how broad its implications are. They tested this on 80 segments from TV debates and community discussions, finding that changes in the memory state, like how many claims are updated, match human perceptions of information gain better than simple measures like how long the utterance is or word frequency counts. They also built AI models using large language models to predict these CIG scores automatically. The approach models an evolving semantic memory where claims are extracted and consolidated incrementally, allowing the system to quantify informational progress through memory-derived dynamics.

Core claim

memory-derived dynamics (e.g., the number of claim updates) correlate more strongly with human-perceived CIG than traditional heuristics such as utterance length or TF--IDF.

Load-bearing premise

The assumption that extracting atomic claims and consolidating them into a structured memory accurately reflects the advancement of collective understanding in the conversation.

Figures

Figures reproduced from arXiv: 2604.15647 by Jey Han Lau, Lea Frermann, Ming-Bin Chen.

**Figure 1.** Figure 1: Overview of the CIG pipeline. Each utterance is evaluated with the Semantic Memory as knowledge context for Novelty, Relevance, Implication Scope, and overall CIG (1–4). The Semantic Memory is maintained through two modules: Extraction, which converts utterances into atomic claims; and Consolidation, which matches extracted claims against the retrieved memory, triggering ADD, UPDATE, or NONE operations. hu… view at source ↗

**Figure 2.** Figure 2: Heatmap of MAE for predicting human utterance-level CIG. The y-axis represents Aspect Combination methods. The x-axis represents Claim Aggregation methods. how well these claim-level predictions can recover human-annotated utterance-level CIG scores. This involves: (i) aggregating aspect-specific scores across multiple claims within an utterance (claimaggregation on the x-axis), and (ii) combining the r… view at source ↗

**Figure 3.** Figure 3: Mean participant CIG (left y-axis) vs. turns [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Annotation interface for segment-level CIG aspect rating. The left panel presents the prior-knowledge [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗

**Figure 5.** Figure 5: Normalized distributions of annotation levels (1–4) per aspect, and by corpus. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗

read the original abstract

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective understanding of the target topic. To operationalize CIG, we model an evolving semantic memory of the discussion: the system extracts atomic claims from utterances and incrementally consolidates them into a structured memory state. Using this memory, we score each utterance along three interpretable dimensions: Novelty, Relevance, and Implication Scope. We annotate 80 segments from two moderated deliberative settings (TV debates and community discussions) with these dimensions and show that memory-derived dynamics (e.g., the number of claim updates) correlate more strongly with human-perceived CIG than traditional heuristics such as utterance length or TF--IDF. We develop effective LLM-based CIG predictors paving the way for information-focused conversation quality analysis in dialogues and deliberative success.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The CIG framework tracks info gain via semantic memory updates and beats simple baselines on 80 segments, but the claim extraction step lacks independent validation.

read the letter

The punchline is that this work gives a plausible new way to quantify information gain in dialogues by tracking how claims evolve in a semantic memory, and the correlations with human judgments are promising but rest on shaky ground. They do something useful by defining CIG along novelty, relevance, and implication scope, then using the memory state to compute dynamics like number of claim updates. Annotating 80 segments from real deliberative talks and showing better correlation than baselines is a concrete step. The LLM-based predictors they develop could be handy for scaling this up. The soft spot is exactly the one in the stress-test: the memory comes from LLM claim extraction and consolidation, but there's no independent check on whether that process accurately reflects what humans see as advancing understanding. If the atomic claims are noisy or incomplete, the whole metric is measuring the parser more than the conversation. The annotation details are thin—no IAA or selection criteria mentioned—which makes the human benchmark less solid than it needs to be. This paper is for NLP folks working on dialogue quality or social scientists studying deliberation who need metrics beyond surface features. A reader looking for fresh ideas on measuring collective knowledge building would find value in the framework, even if they have to fill in the validation gaps themselves. It deserves a serious referee because the idea is original enough and they have some empirical backing, though revisions would be needed to address the memory fidelity issue.

Referee Report

2 major / 2 minor

Summary. The paper introduces a framework for Conversational Information Gain (CIG) that models the informational progress of deliberative dialogues via an evolving semantic memory: atomic claims are extracted from utterances and incrementally consolidated into a structured state. Utterances are then scored along three dimensions (Novelty, Relevance, Implication Scope) derived from memory dynamics such as claim-update counts. The authors annotate 80 segments from TV debates and community discussions, report that these memory-derived metrics correlate more strongly with human-perceived CIG than baselines like utterance length or TF-IDF, and present LLM-based predictors for automated CIG assessment.

Significance. If the semantic memory faithfully captures collective understanding without distortion from claim extraction or consolidation, the work provides a novel, interpretable approach to quantifying informational advancement in public deliberation, extending beyond civility or argument-structure metrics. The reported correlations and LLM predictors could enable scalable, information-focused evaluation in dialogue systems and deliberative analysis.

major comments (2)

[Abstract and Evaluation] Abstract and Evaluation section: The 80-segment annotation study is presented without details on segment selection, annotation guidelines, inter-annotator agreement, or statistical significance of the correlations. This is load-bearing because human CIG judgments serve as the external benchmark for validating that memory dynamics outperform length/TF-IDF; without these, the strength of the headline result cannot be assessed.
[Framework] Framework section on semantic memory construction: The premise that atomic claim extraction plus consolidation produces a lossless representation of collective understanding advancement is untested. If segmentation splits propositions inconsistently or consolidation drops implications, then metrics like claim-update counts become parser artifacts rather than genuine CIG measures; the 80-segment study cannot distinguish these cases from true informational progress.

minor comments (2)

[Framework] Provide explicit formulas or pseudocode for computing the three scoring dimensions (Novelty, Relevance, Implication Scope) from the memory state to improve reproducibility.
[Implementation] Clarify how the LLM pipeline for claim extraction and consolidation is prompted and whether any post-processing rules are applied, as these choices directly affect the derived dynamics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and specify the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract and Evaluation] Abstract and Evaluation section: The 80-segment annotation study is presented without details on segment selection, annotation guidelines, inter-annotator agreement, or statistical significance of the correlations. This is load-bearing because human CIG judgments serve as the external benchmark for validating that memory dynamics outperform length/TF-IDF; without these, the strength of the headline result cannot be assessed.

Authors: We agree that these details are necessary to properly evaluate the annotation study and the strength of our correlations. In the revised manuscript we will expand the Evaluation section with: (i) explicit criteria and sources used to select the 80 segments from the TV debates and community discussions, (ii) the complete annotation guidelines and rating scales provided to annotators, (iii) inter-annotator agreement statistics (e.g., Fleiss’ kappa), and (iv) statistical significance tests (p-values and confidence intervals) for all reported correlations against the baselines. These elements were collected during the study but omitted for space; they will now be reported transparently. revision: yes
Referee: [Framework] Framework section on semantic memory construction: The premise that atomic claim extraction plus consolidation produces a lossless representation of collective understanding advancement is untested. If segmentation splits propositions inconsistently or consolidation drops implications, then metrics like claim-update counts become parser artifacts rather than genuine CIG measures; the 80-segment study cannot distinguish these cases from true informational progress.

Authors: We acknowledge that claim extraction and consolidation are imperfect approximations and that the current study does not directly test their fidelity against a gold-standard memory state. The stronger human correlations relative to length and TF-IDF baselines provide supporting evidence that the derived metrics track perceived informational gain, yet this does not rule out parser artifacts. In the revision we will add a dedicated limitations paragraph in the Framework section that (a) discusses known failure modes of the extraction and consolidation steps with illustrative examples, (b) reports any available extraction-error statistics from our pipeline, and (c) outlines future work on human validation of memory states. This will not change the reported results but will present the modeling assumptions more cautiously. revision: partial

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework relies on assumptions about claim extraction and memory consolidation, with no free parameters explicitly mentioned but the scoring dimensions are defined within the model.

axioms (1)

domain assumption Atomic claims can be reliably extracted from utterances and consolidated into a structured memory state.
This is central to the operationalization of CIG.

invented entities (1)

Semantic memory state no independent evidence
purpose: To track evolving collective understanding
Introduced as part of the framework without external validation mentioned.

pith-pipeline@v0.9.0 · 5470 in / 1096 out tokens · 38451 ms · 2026-05-10T08:58:53.418931+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages

[1]

Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav

Whow: A cross-domain approach for analysing conversation moderation.arXiv preprint arXiv:2410.15551. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. 2025. Mem0: Building production-ready ai agents with scalable long-term memory.arXiv preprint arXiv:2504.19413. Michelene TH Chi. 2009. Three types of conceptual change: Belief r...

work page arXiv 2025
[2]

Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya

Topic-conversation relevance (tcr) dataset and benchmarks.Advances in Neural Information Pro- cessing Systems, 37:140159–140174. Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya. 2022. Novelty detection: A perspective from natural language pro- cessing.Computational Linguistics, 48(1):77–117. Mario Giulianelli, Arabell...

work page 2022
[3]

Evaluating human-language model interaction.arXiv preprint arXiv:2212.09746,

Hearing personal experiences improves so- cial evaluations compared to personal opinions, espe- cially for polarized parties.Especially for Polarized Parties (December 05, 2023). Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, and Diyi Yang. 2024. Silent sig- nals, loud impact: Llms for word-sense disambigua- tion of coded dog whi...

work page arXiv 2023
[4]

Death of the

Six attributes of unhealthy conversations. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 114–124. Kun Qian, Maximillian Chen, Siyan Li, Arpit Sharma, and Zhou Yu. 2025. Bottom-up synthesis of knowledge-grounded task-oriented dialogues with iteratively self-refined prompts. InProceedings of the 2025 Conference of the Nations of the...

work page arXiv 2025
[5]

Speaker 1: I hope my kids own guns

work page
[6]

Target utterance:

Speaker 2: I am thinking the opposite. Target utterance:

work page
[7]

memories

Speaker 3: When I look at the statistics about how that adds to the risk of suicide, the risk of being misused, the risk of it being stolen, used in a domestic quarrel, I think it’s just too much of a risk. Output: {"memories":[ {"speaker":"Speaker 3","target_speaker":"Everyone","claim":"Having a gun increases the risk of suicide.","turn_id":"3"}, {"speak...

work page
[8]

same speaker &equivalent

work page
[9]

same speaker &backward_entail

work page
[10]

same speaker & (contradictionorforward_entail)

work page
[11]

memory_updates

different speaker & any non-neutral relation Else: no eligibleB→treat as neutral (ADD, target=null). Ties within a rung: pick the highest confidence (or highest similarity). Action mapping Same speaker: equivalent,backward_entail → NONE;forward_entail,contradiction → UPDATE;neu- tral→ADD. Different speaker: always ADD. UPDATE semantics Ifcontradiction: re...

work page arXiv

[1] [1]

Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav

Whow: A cross-domain approach for analysing conversation moderation.arXiv preprint arXiv:2410.15551. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. 2025. Mem0: Building production-ready ai agents with scalable long-term memory.arXiv preprint arXiv:2504.19413. Michelene TH Chi. 2009. Three types of conceptual change: Belief r...

work page arXiv 2025

[2] [2]

Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya

Topic-conversation relevance (tcr) dataset and benchmarks.Advances in Neural Information Pro- cessing Systems, 37:140159–140174. Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya. 2022. Novelty detection: A perspective from natural language pro- cessing.Computational Linguistics, 48(1):77–117. Mario Giulianelli, Arabell...

work page 2022

[3] [3]

Evaluating human-language model interaction.arXiv preprint arXiv:2212.09746,

Hearing personal experiences improves so- cial evaluations compared to personal opinions, espe- cially for polarized parties.Especially for Polarized Parties (December 05, 2023). Julia Kruk, Michela Marchini, Rijul Magu, Caleb Ziems, David Muchlinski, and Diyi Yang. 2024. Silent sig- nals, loud impact: Llms for word-sense disambigua- tion of coded dog whi...

work page arXiv 2023

[4] [4]

Death of the

Six attributes of unhealthy conversations. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 114–124. Kun Qian, Maximillian Chen, Siyan Li, Arpit Sharma, and Zhou Yu. 2025. Bottom-up synthesis of knowledge-grounded task-oriented dialogues with iteratively self-refined prompts. InProceedings of the 2025 Conference of the Nations of the...

work page arXiv 2025

[5] [5]

Speaker 1: I hope my kids own guns

work page

[6] [6]

Target utterance:

Speaker 2: I am thinking the opposite. Target utterance:

work page

[7] [7]

memories

Speaker 3: When I look at the statistics about how that adds to the risk of suicide, the risk of being misused, the risk of it being stolen, used in a domestic quarrel, I think it’s just too much of a risk. Output: {"memories":[ {"speaker":"Speaker 3","target_speaker":"Everyone","claim":"Having a gun increases the risk of suicide.","turn_id":"3"}, {"speak...

work page

[8] [8]

same speaker &equivalent

work page

[9] [9]

same speaker &backward_entail

work page

[10] [10]

same speaker & (contradictionorforward_entail)

work page

[11] [11]

memory_updates

different speaker & any non-neutral relation Else: no eligibleB→treat as neutral (ADD, target=null). Ties within a rung: pick the highest confidence (or highest similarity). Action mapping Same speaker: equivalent,backward_entail → NONE;forward_entail,contradiction → UPDATE;neu- tral→ADD. Different speaker: always ADD. UPDATE semantics Ifcontradiction: re...

work page arXiv