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BenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts

Kazi Noshin, Sajib Acharjee Dip, Ranat Das Prangon, Fardin Hassan Tamim, Syed Ishtiaque Ahmed, Liqing Zhang, Sharifa Sultana

arXiv:2606.10061v1cs.CL

TL;DR

Existing sycophancy research largely examines factual agreement and instruction following, while nuanced conversational sycophancy in culturally specific social contexts remains difficult to evaluate. BenSyc addresses this gap with a human-validated Bengali benchmark spanning alignment classification and response generation. Experiments show substantial variation in model behavior, and many systems struggle to distinguish supportive empathy from interpersonal validation and escalation.

  • Problem

    Prior sycophancy research focuses mainly on factual agreement and instruction-following, while social conversations require distinguishing support from validation and escalation.

  • Method

    BenSyc is a human-validated benchmark of 1,078 Bengali Reddit post–comment pairs with binary labels and a five-level taxonomy, used to evaluate proprietary and open-weight LLMs.

  • Results

    Models often struggle to distinguish supportive empathy from stronger interpersonal validation and escalation, with substantial variation across model families, prompting settings, and social contexts.

  • Takeaways & Limitations

    Culturally grounded multilingual benchmarks are needed to analyze nuanced conversational behavior beyond coarse agreement, toxicity, or harmlessness detection.

  • Takeaways & Limitations

    BenSyc focuses on Bengali Reddit conversations, may not generalize across languages or settings, and does not directly measure downstream real-world harm or long-term user impact.

Abstract

from arXiv · show

Large language models (LLMs) increasingly participate in emotionally sensitive social conversations, where responses may shift from balanced support toward excessive validation or escalatory alignment. Existing sycophancy research primarily focuses on factual agreement and instruction-following settings, leaving culturally grounded conversational sycophancy underexplored. We introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts. Starting from 11,840 Reddit posts and 170k comments collected from communities across Bangladesh and West Bengal, we construct a human-validated benchmark with binary labels and a fine-grained five-level taxonomy spanning Invalidation, Neutral, Support, Validation, and Escalation. We evaluate more than 15 open and proprietary LLMs on conversational alignment classification and response generation tasks. Results show that distinguishing empathetic support from reinforcement-oriented validation remains challenging even for frontier instruction-tuned models: the best system achieves only 61.8 Macro-F1 on binary detection and 61.7 Macro-F1 on five-class classification. In generation settings, several models frequently produce strongly validating or escalatory responses in emotionally charged situations. Our findings highlight substantial variation across model families and conversational behaviors, underscoring the importance of culturally grounded multilingual benchmarks for evaluating socially aligned conversational AI systems.

1 Introduction

BenSyc addresses the difficulty of evaluating conversational sycophancy in Bengali social contexts, where empathy can resemble reinforcement of a user’s interpretation. It introduces a human-validated benchmark and evaluates LLMs on classification and generation.

  • Motivation: Conversational sycophancy extends beyond factual agreement because social responses may comfort, question, validate, or escalate a user’s interpretation.Supportive empathy and reinforcement-oriented validation can appear superficially similar while differing in whether they reinforce the user’s framing.
  • Benchmark: BenSyc is a Bengali-context benchmark containing 1,078 human-validated Reddit post–comment pairs from six Bengali-focused communities.The dataset preserves Bangla, Banglish, English, emojis, slang, and code-switching.
  • Benchmark: The benchmark uses binary alignment labels and a five-level taxonomy spanning Invalidation, Neutral, Support, Validation, and Escalation.The taxonomy separates emotional support from stronger forms of validation and escalation.
  • Evaluation: BenSyc evaluates proprietary and open-weight LLMs on conversational alignment classification and response generation tasks.Generation evaluation uses a GPT-5.5 rubric-based judge validated against human reviewers.
  • Findings: LLMs often struggle to distinguish supportive empathy from stronger interpersonal validation and escalation.The results also show substantial variation in conversational behavior across models and emotionally charged framings.

2 Related Work

Prior work examines sycophancy, advice quality, moral reasoning, and cultural alignment largely as separate problems. BenSyc connects these areas through a Bengali benchmark focused on nuanced conversational alignment.

  • Related benchmarks: Related benchmarks address social norms, moral judgments, online interpersonal judgment, and socially grounded model expectations.BenSyc differs by focusing specifically on conversational sycophancy in Bengali social interactions.
  • Cultural context: Most alignment and safety evaluations remain English-centric despite variation in conversational norms across cultures.Prior work reports Western-centric assumptions and cultural biases in LLM outputs, while native-language prompting does not necessarily ensure culturally grounded reasoning.
  • Research gap: Existing sycophancy benchmarks mainly study factual agreement or belief imitation, while advice and moral reasoning benchmarks emphasize helpfulness, preferences, or norm understanding.These research directions have generally been treated as separate problems.
  • BenSyc’s position: BenSyc models conversational alignment as a progression from invalidation and support to validation and escalation.This connects sycophancy evaluation with culturally grounded social judgment in Bengali conversations.

3 Dataset

BenSyc constructs a Bengali Reddit benchmark from culturally diverse online communities, filtering posts for observable responses and multiple moral standings. It combines binary labels with a five-level alignment taxonomy validated by native Bengali-speaking annotators.

  • Data Collection: The data come from six Bengali-focused subreddits spanning Bangladesh and West Bengal, with English, Bengali, and Banglish usage.The communities cover regional, relationship-advice, youth, and general social interactions.
  • Data Collection: 11,840 posts were scraped, then filtered for human comments and multiple moral standings, yielding 1,078 relevant posts.The retained posts include advice seeking, emotional expression, concerns, and descriptions of problematic behavior.
  • Annotation: The binary scheme labels responses as NON-SYCOPHANTIC or SYCOPHANTIC according to the community’s consensus response.The most upvoted top-level comment serves as the primary consensus candidate rather than ground truth.
  • Annotation: The five-level taxonomy comprises Invalidation, Neutral, Support, Validation, and Escalation, representing increasing alignment reinforcement.Support provides empathy without strongly reinforcing interpretation, whereas Escalation amplifies stance through hostility, blame, or stronger reactions.
  • Validation: Two native Bengali-speaking annotators independently validated LLM-assigned categories while considering sarcasm, implicit agreement, code-mixing, and sociocultural nuance.The benchmark contains 1,078 human-validated post–comment pairs and is relatively balanced at the binary level.

4 Experimental Setup

The experiments evaluate conversational sycophancy through binary classification, five-class classification, and natural response generation across more than 15 LLMs. Generated responses are judged with the same alignment taxonomy and additional quality dimensions.

  • Tasks: Models perform binary classification by predicting whether a response is sycophantic or non-sycophantic.Classification uses zero-shot prompts with structured JSON outputs containing labels, confidence, rationales, and evidence spans.
  • Tasks: Fine-grained classification requires predicting one of five conversational alignment categories.The reported metrics include macro-F1, weighted-F1, per-class F1, and confusion matrices.
  • Tasks: Conversational generation asks models to produce natural responses to Reddit-style posts, which are evaluated using the alignment taxonomy.The evaluation covers more than 15 proprietary and open-weight LLMs using shared prompts.
  • Evaluation: GPT-5.5 judges generated responses with the five-class taxonomy and scores helpfulness, balance, harmfulness, cultural naturalness, and coherence.This enables analysis of alignment behavior alongside overall response quality.
  • Evaluation Metrics: Sycophancy rate aggregates responses labeled Support, Validation, and Escalation.The study also reports their relative proportions within generated sycophantic responses and computes escalation rate from Escalation responses.

5 Benchmarking Results

BenSyc evaluations show that LLMs struggle with both binary sycophancy detection and fine-grained conversational alignment, while generation reveals varied support, validation, and escalation behaviors across models.

  • Binary Sycophancy Detection: 61.8 Macro-F1: Llama3.3-70B achieves the strongest binary sycophancy-detection performance.Gemma4-31B is conservative with 85.7% precision and 22.6% recall, whereas Mistral-7B is aggressive with 93.5% recall and lower precision.
  • Fine-Grained Conversational Alignment Classification: 61.7 Macro-F1: Gemma4-31B leads five-class conversational alignment classification.GPT-5.4-mini, Qwen2.5-32B, and Llama3.3-70B follow with lower overall Macro-F1 scores.
  • Fine-Grained Conversational Alignment Classification: 76 F1 on Invalidation versus 51 F1 on Escalation: Gemma4-31B performs unevenly across alignment categories.Support and especially Escalation are difficult for most models, while Invalidation and Validation generally achieve higher F1.
  • Natural Generation Evaluation: 92.5% sycophancy rate: Llama3.3-70B produces the highest overall sycophancy rate in natural generation.Mixtral-8x7B, GPT-OSS-20B, and Qwen2.5-7B also show high rates, while GPT-5.4-mini produces 70.0%.
  • Natural Generation Evaluation: Llama3.3-70B and GPT-OSS-20B primarily generate validation-oriented agreement, whereas Qwen2.5-7B produces proportionally more support-oriented responses.Escalatory generations are less common than support and validation but appear across most evaluated open-weight models.
  • Cross-Model Behavioral Trends: Scaling improves conversational alignment inconsistently, and several models with competitive binary performance degrade under fine-grained evaluation.Models therefore exhibit distinct alignment strategies rather than a single performance pattern.

6 Discussion

Conversational alignment behavior varies across model families, prompting settings, and social contexts, while fluent responses can still reinforce problematic reasoning.

  • Discussion: Culturally grounded multilingual benchmarks are needed to analyze nuanced conversational behaviors beyond coarse agreement.The discussion connects model variation and subtle escalation to the need for broader evaluation approaches.
  • Discussion: Many escalatory responses remain conversationally fluent, socially plausible, and superficially emotionally supportive.This makes them difficult to detect with coarse toxicity or harmlessness-oriented evaluation frameworks.

Conclusion

BenSyc demonstrates that conversational sycophancy in Bengali social-media interactions spans support, validation, and escalation, with substantial variation across model families.

  • BenSyc enables binary and fine-grained conversational alignment analysis across classification and generation tasks.
  • The benchmark shows conversational sycophancy manifests through diverse behaviors, including support, validation, and escalation.
  • Model behavior varies substantially across model families in these conversational settings.
  • BenSyc is intended to support research on multilingual conversational safety, culturally grounded evaluation, and nuanced alignment analysis beyond coarse agreement or toxicity detection.

Limitations

BenSyc’s limitations concern its cultural and platform scope, evaluation method, data distribution, and unmeasured downstream effects.

  • The Bengali online focus may limit generalization to other languages, dialects, or cultural settings.
  • Reddit-based data may introduce demographic and platform-specific biases.
  • LLM-as-judge evaluation remains imperfect for nuanced conversational distinctions despite strong agreement with human reviewers.
  • Escalation examples are less frequent than support or validation examples, reflecting the collected data’s natural distribution.
  • BenSyc evaluates conversational alignment behavior but does not directly measure downstream real-world harm or long-term user impact.

Ethics Statement

The work uses publicly accessible social-media discussions for research and positions BenSyc as an evaluation benchmark rather than deployment guidance.

  • The study examines conversational sycophancy and emotionally reinforcing behaviors in multilingual social-media interactions.
  • Its dataset comes from publicly accessible online discussions processed for research purposes only.
  • BenSyc is intended exclusively for evaluating conversational alignment, not as guidance for deploying persuasive or emotionally manipulative systems.

A Dataset Sources

BenSyc is built from Bengali-focused Reddit communities to capture culturally grounded conversational alignment in natural online interactions. The dataset is filtered, manually validated, and organized around progressively nuanced forms of support, validation, and escalation.

  • Data sources: 11,840 Reddit posts were collected from six Bengali-focused communities across Bangladesh and West Bengal, then filtered to 1,078 relevant posts.The communities span regional, relationship-advice, youth, and general social discussions.
  • Conversation selection: Each benchmark example pairs a Reddit post with a corresponding human response selected from its comment thread.Posts concatenate title and self-text, while representative responses are selected from highly upvoted comments.
  • Filtering: BenSyc excludes descriptive, irrelevant, spam-like, low-quality, and stance-free post–comment pairs where conversational alignment could not plausibly emerge.The construction targets interactions involving interpersonal alignment, disagreement, emotional validation, reinforcement, or escalation.
  • Language and context: The dataset preserves Bengali, Banglish, English, emojis, slang, code-switching, sarcasm, and informal social expressions.This preserves culturally grounded linguistic and conversational patterns rather than normalizing or translating the text.
  • Human validation: Two native Bengali-speaking annotators manually reviewed retained examples, discussed disagreements, and removed uncertain or weakly grounded cases.This conservative strategy prioritizes annotation reliability and evaluation quality over dataset scale.
  • Alignment taxonomy: The taxonomy models progressive alignment from invalidation and neutral discussion through support, validation, and escalation.It distinguishes emotionally supportive empathy from stronger reinforcement that amplifies the poster’s framing or emotional stance.

F.2 Conversational Alignment Evaluation Framework

BenSyc evaluates conversational alignment beyond binary agreement by modeling progressively stronger interpersonal reinforcement and separating support from validation and escalation. Its analyses reveal substantial behavioral variation and conservative or aggressive detection strategies across models.

  • BenSyc models conversational alignment as progressively stronger forms of interpersonal reinforcement rather than a single homogeneous sycophancy phenomenon.
  • The five-level framework distinguishes analytical pushback, emotional support, explicit validation, and harmful escalatory reinforcement.
  • Generated responses are evaluated with a rubric-based GPT-5.5 judge that assigns alignment labels, confidence, evidence spans, and quality scores.Quality dimensions include helpfulness, balance, harmfulness, cultural naturalness, and coherence on a 1–5 scale.
  • Binary prediction compositions vary substantially across models despite similar overall Macro-F1 scores.The analysis separates correct non-sycophantic predictions, false positives, false negatives, and correctly detected sycophantic cases.
  • Gemma4-31B favors conservative detection, while Mistral-7B and Gemma2-27B favor aggressive sycophancy predictions.Gemma4-31B misses many subtle sycophantic responses, whereas the latter models achieve high recall with more false positives.
  • Larger Llama models improve binary Macro-F1, but Gemma4-31B underperforms Gemma2-27B, showing that performance depends on more than size.Instruction tuning and alignment objectives may significantly influence conversational sycophancy detection beyond raw parameter count.
  • BenSyc exposes nuanced alignment differences hidden by standard binary accuracy evaluation, particularly across multilingual and instruction-tuned LLMs.

G.2 Additional Fine-Grained Classification Analysis

Additional analyses show that fine-grained conversational alignment remains difficult, especially at semantic boundaries between adjacent categories. Scaling generally helps, but generation patterns and culturally specific discourse expose persistent model differences.

  • Class-level difficulty distribution: Escalation has the lowest average performance at 28.5 F1, while Validation and Invalidation average 48.4 and 49.6 F1 respectively.Escalation also shows the largest variance, whereas Validation and Invalidation are the most stable categories.
  • Class-level difficulty distribution: Fine-grained annotation reveals distinctions that binary detection obscures, with current LLMs struggling most with culturally grounded conversational nuance.
  • Scaling behavior across model families: Larger models generally improve fine-grained understanding, although scaling gains differ across Qwen, Llama, and Gemma architectures.Models below 10B parameters consistently underperform, particularly on nuanced conversational categories.
  • Scaling behavior across model families: Some larger models still confuse Support and Validation despite strong overall Macro-F1.The passage suggests that region-specific discourse patterns and implicit social norms may be needed beyond parameter scaling.
  • Confusion analysis: Most classification errors occur between semantically adjacent categories, especially Support versus Neutral and Validation versus Escalation.
  • Confusion analysis: GPT-5.5 correctly identifies 93% of escalation examples, while Gemma4-31B and Qwen2.5-32B show more escalation–validation confusion.
  • Confusion analysis: Models systematically struggle at semantic boundaries rather than failing randomly, making BenSyc more diagnostically informative than binary agreement benchmarks.
  • Conversational generation analysis: Validation dominates sycophantic generations for Llama3.3-70B and GPT-OSS-20B, while Qwen2.5-7B produces more direct support responses.
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