Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
Authors: Ce Zhang
Zifu Wan
Zhehan Kan
Martin Q. Ma
Simon Stepputtis
Deva Ramanan
Russ Salakhutdinov
Louis-Philippe Morency
Katia P. Sycara
Yaqi Xie
Venue: ICLR 2025 Poster (2025)
Keywords: Large Vision-Language Models
Hallucinations
Generative Feedback
Primary area: foundation or frontier models, including LLMs
TL;DR: We introduce self-correcting Decoding with Generative Feedback (DeGF), a novel training-free algorithm that incorporates feedback from text-to-image generative models into the decoding process to effectively mitigate hallucinations in LVLMs.

Abstract: While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not align with the given visual input, which restricts their practical applicability in real-world scenarios. In this work, inspired by the observation that the text-to-image generation process is the inverse of image-conditioned response generation in LVLMs, we explore the potential of leveraging text-to-image generative models to assist in mitigating hallucinations in LVLMs. We discover that generative models can offer valuable self-feedback for mitigating hallucinations at both the response and token levels. Building on this insight, we introduce self-correcting Decoding with Generative Feedback (DeGF), a novel training-free algorithm that incorporates feedback from text-to-image generative models into the decoding process to effectively mitigate hallucinations in LVLMs. Specifically, DeGF generates an image from the initial response produced by LVLMs, which acts as an auxiliary visual reference and provides self-feedback to verify and correct the initial response through complementary or contrastive decoding. Extensive experimental results validate the effectiveness of our approach in mitigating diverse types of hallucinations, consistently surpassing state-of-the-art methods across six benchmarks. Code is available at https://github.com/zhangce01/DeGF.

Published as a conference paper at ICLR 2025
SELF-CORRECTING DECODING WITH GENERATIVE FEEDBACK FOR MITIGATING HALLUCINATIONS IN LARGE VISION-LANGUAGE MODELS
Ce Zhang Zifu Wan'1 Zhehan Kan Martin Q. Ma¹ Simon Stepputtis'
Deva Ramanan' Russ Salakhutdinov" Louis-Philippe Morency' Katia Sycara'
Yaqi Xie
SScool of Computer Science, Carnegie Mellon University SShenzhen International Graduate School, Tsinghua University
ABSTRACT
While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone generating hallucinatory text responses that do not align with the given visual input, which restricts their practical applicability in real-world scenarios. In this work, inspired by the observation that the text-to-image generation process is the inverse of image-conditioned response generation in L.L.M,, we explore the potential of leveraging text-to-image generative models to assist in mitigating hallucinations in LVLMs. We discover that generative models can offer valuable self-feedback for mitigating hallucinations at both the response and token levels. Building this insight, we introduce self- ceff-corret Decoding with Generative Feedback (DeGF), a novel training-free free algorithm that incorporates feedback from text-to-image generative models into the decoding process to effectively mitigate hallucinations in LVLMs. Specifically, DeGF generates image from the initial response produced by LVLMs, which acts as an auxiliary visual reference and provides self-feedback to verify and correct the initial response through complementary or contrastive decoding. Extensive experimental results validate the effectiveness of our approach in mitigating diverse types of hallucinations, consistently surpassing state-of-the- methods across six benchmarks. Code is available at https: //s //githutt com/ /tt///ccaaa/eeeeEE
INTRODUCTION
Large Vision- Visguu-Language Models (LVLMs) have demonstrated remarkable performance across various multi-modal tasks, such as image captioning and visual question answering, by extending the capabilities of powerful Large Language Models (LLMs) to incorporate visual inputs (Liu al., 2023; Li et al., 2023b; Dai et al., 2023; Bai et al., 2023; Ye et al., 2024). Despite their proficiency in interpreting both visual and textual modalities, these models often suffer from hallucinations, where LVLMs erroneously produce responses that are inconsistent with the visual input (Li et al., 2023d; Gunjal et al., 2024; Yin et al., 2023; Wu et al., 2024). This potential for misinformation raises significant concerns, limiting the models' reliability and restricting their broader deployment in real-world scenarios (Liu et al., 2024b; Bai et al., 2024; Chen et al., 2024b; Zhao et al., 2024).
Recent research has revealed that major cause of hallucinations in LVLMs is the over-reliance on language priors due to biased training sets, which can override the visual content in response generation (Bai et al., 2024; Liu al., 2024b; Leng al., 2024). In response, various strategies have been developed to detect and mitigate these hallucinations by directly introducing additional training (Chen al., 2024a; Sun al., 2023; Jiang al., 2024; Chen al., 2023; Zhang et al., 2024), demonstrating promising results in reducing over-reliance. However, the need for additional data and costly traiming processes hinders their deployment in downstream tasks. More recently, new paradigm of methods has emerged to tackle the hallucination problem in LVLMs by intervening in the decoding process (Huang et al., 2024; Deng et al., 2024; Kim et al., 2024). Among these, recent training-fire ffree contrastive decoding based methods (Li et al., 2023c) have proven effective in mitigating undesired hallucinations by contrasting token predictions derived from original visual input with bias-inducing counterparts, such no/distorted visual input (Favero et al., 2024; Leng et al., 2024), disturbed instructions (Wang et al., 2024), or premature layers (Chuang et al., 2024).
"Equal contribution. Contact: {cezhang, zifuu, yaqix}0cs. cmu, edu.

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Original Imaige P
L. Response (based v) The image features bunnies (or rabbits)
Origiall Image P
LLaVA. 1.5 Response (based on The Image features collection of four white
the main animals. They are both sitting at
coffee mugs cach adorned a different
table see for picnic, enjoying each other's
Mario character from the popular video game series.
company and food.
x
X
Stable Diffusion
Stable Diffusion
Generatod Image
Corrected Response (based v and n))
Generated Image
Corrected Response (based on and v')
image features a group of three animals. including bear, a cat, and a rabbit, They are sitcing around a table, likely eating cookies, with plates of food in front of chem.
The image features collection three white mugs placed row, each featuring well- known video game character. Mario, from the popular Nintenda series.
Figure 1: Generative models can visualize and help correct various types of hallucinations in the initial response. I In the first query, we provide LLaVA-1.5 (Liu et al., 2023) with the prompt "Describe this image in detail" to produce captions for two examples from LLa Ve-ench.. Based on the initial response, we utilize Stable Diffusion XL (Podell et al., 2024) to generate a new image v', which effectively highlights hallucinations and provides valuable self-feedback. the second query, our approach incorporates both the original image v and the generated image v' into the decoding process, using the feedback successfully correct various types of hallucinations.
In
While these contrastive decoding- becsddng-based methods effectively mitigate hallucinations arising from language priors, we recognize that hallucinations can also originate beyond language bias, stemming from visual deficiencies in LVLMs (Tong et al., 2024). For instance, in counting hallucinations, lan- guage does not imply any count information; instead, miscounts largely arise from visual recognition errors of LVLMs, complex scenes include numerous, similar objects at ambiguous positions which may confuse the LVLMs, leading to incorrect visual understanding and, consequently, hallucinated answers. Therefore, argue that current contrastive decoding-based methods may struggle to generalize effectively across different types of hallucinations.
In this work, explore the potential of leveraging powerful text-to-image generative models (e.g., Stable Diffusion (Rombach et al., 2022; Podell et al., 2024)) to mitigate various types of hallucinations in LVLMs. Our work is based on a simple yet intuitive hypothesis: Given a visual input and textual prompt to an LVLM, if the generated response conditioned on the original image is accurate and non- hallucinatory, a text-to-image generative model should be capable of reversing this process to produce similar image from that response. Alternatively, if there is a discrepancy between the original image and the one generated from the response, this difference can serve as valuable self-feedhack, guiding the decoding process to correct potential hallucinations in the initial response. To verify this hypothesis, we conduct an empirical study (in Section 3.2), demonstrating that generative models can provide valuable self fellffeebback// for mitigating hallucinations at both the response and token levels.
Building on this insight, we introduce self-correcting Decoding with Generative Feedback (DeGF), novel training- free decoding algorithm that effectively incorporates feedback from text-to-image generative models to recursively enhance the accuracy of LVLM responses. Specifically, for each instance, generate new image based the initial response, which serves an auxiliary visual reference to assess and verify the accuracy of the initial output. We propose self-correcting decoding that either enhances or contrasts predictions from the original and this reference based on the auxiliary visual reference, confirming or revising the initial LVLM response based on the degree of divergence between the two predictions. By integrating this additional visual reference and generative feedback, LVLMs can gain enhanced visual insights and verify the initial response to ensure accurate visual details in the text outputs. In Figure 1, we demonstrate that incorporating generative feedback in our approach can reduce various types of hallucinations, including object existence, visual appearance, counting, etc. To the best of our knowledge, we are the first work to explore the use of text-to-imaae generative feedback as self-correcting mechanism for mitigating hallucinations in LVLMs.
The effectiveness of DeGF is evaluated on LLaVA-1.55 InstructBLIP, and Qwen-VI across six benchmarks: POPE (Li et al., 2023d), CHAIR (Rohrbach et al., 2018), MMllllalliinationnn (Fu et al., 2023), MMBench (Liu et al., 2024d), MMVP (Tong et al.. 2024), and LLaVA- -Bncch... Extensive experimental results validate the effectiveness of our DeGF in mitigating various types of halluci- nations in LVLMs. Qualitative case studies and a-iddV evaluation on LLa VA-BAABBe further demonstrate that our approach enhances both the accuracy and detailedness of the LVLM responses.
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The contributions of this paper are summarized follows:
We investigate the potential of text-to-image generative models in mitigating hallucinations in LVLMs and demonstrate that text-to-image generative models can provide valuable self-feedback for mitigating hallucinations at both the response and token levels.
We propose self-correcting Decoding with Generative Feedback (DeGF), a novel training-free decoding algorithm for LVL.Ms that recursively enhances the accuracy of responses by integrating feedback from text-to-image generative models with complementaar//ontrssivv decoding.
Extensive experimental evaluations across six benchmarks demonstrate that our DeGF consistently outperforms ootte-of-thearr approaches in effectively mitigating hallucinations in LVL.Ms.
RELATED WORK
Hallucination in LVLMs. With advances of autoregressive LLMs (Touvron et al., 2023: Chowdhery et al., 2023; Chiang et al., 2023), researchers have extended these powerful models to process visual inputs, leading to the development of LVLMs (Liu et al., 2023; Dai et al., 2023; Bai et al., 2023; Ye et al., 2024). These models typically train a modality alignment module to project visual tokens into the textual embedding space of the LLM, demonstrating impressive performance in various multi-modal tasks such as visual question answering and image captioning (Liu et al., 2024b; Bai et al., 2024). However, LVLMS are prone to hallucinations, where contradictions arise between the visual content and the generated textual response (Li et al., 2023d; Liu et al., 2024b; Bai et al., 2024).
To mitigate hallucinations in LVLMs, early works have introduced various approaches, including reinforcement learning from human feedback (RLHF) (Gunjal et al., 2024; Sun et al., 2023), applying auxiliary supervision (Jiang et al., 2024; Chen et al., 2023), incorporating negative (Liu et al., 2024a) or noisy data (Yue et al., 2024), and training post-hoc revisors for correction (Zhou et al., 2024; Yin et al., 2023). Despite promising results, these methods often lack practicality due to their reliance on additional data and costly training processes. To address this, another line of work focuses on training- free methods that can be seamlessly integrated into existing LVLMs. Such methods encompass contrastive decoding (Leng et al., 2024; Favero et al., 2024) and guided decoding with auxiliary information (Chen et al., 2024d; Deng et al., 2024; Woo et al., 2024). In this work, we present a novel training-fres free approach that recursively enhances the accuracy of the LVLM response by incorporating text-to-image generative feedback. To the best of our knowledge, are the first work to effectively utilize feedback from text-to-image generative models to mitigate hallucinations in LVLMs.
Text- Text-to-Image Synthesis. Text-to-image synthesis aims to create realistic images from textual descriptions (Zhu et al., 2019; Ge et al., 2023). In recent years, significant progress has been achieved in this area, largely due to the advent of deep generative models (Zhan et al., 2023; Goodfellow et al., 2014). These advances include Generative Adversarial Networks (GAN) (Sauer et al., 2023; Kang et al., 2023), autoregressive models (Chang al., 2023; Yu et al., 2022), and diffusion models (Ho et al., 2020; Karras et al., 2022; Nichol et al., 2022: Saharia et al., 2022; Rombach et al., 2022). Among these, diffusion-basee methods have been particularly distinguished due to their ability to generate high-quality, detailed images with fine-grained control over the synthesis process (Yang al., 2023; Croitoru al., 2023). Pre-trained on large-scale text-image datasets such as LAION (Schuhmann et al., 2022), diffusion- language alignment, making them valuable for downstream tasks such as classification (Li et al., 2023a) and semantic segmentation (Amit et al., 2021; Wolleb et al., 2022).
methods have demonstrated strong vision-
More recently, Jiao et al. (2024) incorporate text-to-image generative models to enhance fine-grained image recognition in LVLMs by introducing the Img-Diff dataset, which generates pairs of similar images using Stable Diffusion XL (Podell et al., 2024). Their results demonstrate that fine-tuning LVLMs with this additional data leads to improved performance on several VQA tasks. In contrast, in this work, we directly leverage a pre-trained diffusion model to provide valuable self-feedback for refining the generated responses of LVLMs in the decoding process, dynamically improving the accuracy and consistency of the model's 's response without modifying the underlying LVLMS.
3 METHOD
In this work, we present DeGF, a novel training- ffre algorithm that recursively improves the accuracy of LVLM responses using text-to-image generative feedback, illustrated in Figure 2.
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voual laxarta loctial tokarta
Response
Large Vision- Language Models
image features cat sitting on rock
Generative Models (eg. Stable Diffusion)
duckling..
Ougut takens:
Original Image
Input Describe image.
[x (xx.y......
Generated 1/
Contrastive Dacading
Next Tokt- Predictions
10p>
three
Large Vision- Language
severot
J5 Divengence
tab
Dy
three
Camplamentary Deccding
several
Curput
tso
Final Response
three
Generated Image
Ongoing Response y The image surrounded by.
The image features
several
Self-Correcting Decoding
surrounded two ducks near a water fall..
Figure 2: Overview of proposed DeGF. Our method follows two-step process: first, generative model produces a high-quality image based on the initial response; second, this image acts auxiliary visual reference, providing feedhack refine the next-token predictions. Additionally, we introduce self-correcting decoding, which either enhances or contrasts the next-token predictions conditioned on the original and generated images to mitigate hallucinations the LVLM response.
3.1 PRELIMINARY: DECODING OF LVLMS
We consider an LVLM parameterized by 6, which processes an input image and textual query x, aiming to autoregressively generate fluent sequence of textual responses y. The visual input is first processed by vision encoder and then projected into visual tokens within the textual input space using a lisionnlanguage alignment module (e.g., Q-Former (Li et al., 2023b) or linear projection (Liu al., 2023)). These visual tokens, along with the textual query tokens, then fed into the language encoder for conditioned autoregressive generation. We denote the autoregressive generation process as
Y/1 ~ po uu, yet) x exp fo(v:lu x,, x,),
(1)
where Yt represents the token at time step f, Y<t e [yo..... Ut-1] denotes the sequence of tokens generated before time step i, and fo is the logit distribution (unnormalized log-probabilities) [0, .0,.... T). the
produced by the LVLM over vocabulary of textual tokens V. At each step response token y, is sampled from the probability distribution pa (yi (V, /,,,,,, yx,yc yt). and this generative process continues iteratively until the response sequence P [Jo...., yr] is complete.
3.2 VISUAL REFERENCE GENERATION
In our method, we incorporate generative feedback from diffusion models to guide the decoding process. Specifically, given visual input and a textual query we first prompt the LVLMs to generate an initial response т, which includes relevant descriptions of the visual input with potential hallucinations. Subsequently, we leverage pre-trained diffusion model G to generate new image v' based on the initial response: o' D'=G(T where ØT ~ (0,
(2)
Here, #T denotes a sample from the standard Gaussian distribution, which serves as the initial noisy input the diffusion model. Starting from this pure noise image Ir, the diffusion model g iteratively applies steps of the denoising process to obtain Fr, #r-1.... Po. where the final output To corresponds to the final generated image v'. Through this diffusion process, the generative model visualizes the initial response, providing a visual reference that helps mitigate potential hallucinations and produce a more accurate and consistent output.
Effectiveness of Text-to-Image Generative Models in Reflecting Hallucinations, We validate the effectiveness of generative models in reflecting hallucinations through an empirical study, as shown in Figure 3.1 The experimental results demonstrate that text-to-image generative models can provide valuable self- seld-fee for mitigating hallucinations at both the response and token levels.
For Figure evaluate 1,000 CHAIR samples (Left) and 3,000 POPE samples (Right).
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We conduct the following two exper- iments: (1) We generate an image v' using diffusion model based the initial caption provided by LLaVA- 1.5 and compute the CLIP image sim- ilarities between the original image and the generated image v' using OpenCLIP (Cherti et al., 2023) ViT- H/14 backbone. Following prior work, we use the CHAIR (Rohrbach et al., 2018) benchmark, a rule-based metric on MS-COCO (Lin et al., 2014) for evaluating object hallucination from generated captions. We report the average per-instance metric CHAIR, within each bin of CLIP similarity, which evaluates the object hallucina- tion rates in the entire initial response.
CHAR
Non Halluchnatory Tokcrs
Hatuec natery lokers
CLP Smilarit (Scate by 10Dn
Divergencc
Figure 3: Text-to-image generative models can provide feedback for reflecting hallucinations. (Left) Bar plot of average CHAIR, scores binned by CLIP similarity (scaled by 100) on the CHAIR benchmark; (Right) Density plots of token-level JS divergence for both hallucinatory and non- hallucinatory tokens on the POPE benchmark.
As shown in Figure 3 (Left), clear negative correlation between hallucination rates and CLIP sim- ilarities is observed (with a correlation coefficient of p -0.63). This indicates that lower similarity between the original image and generated image corresponds to higher rates of hallucinations at the response level. (2) Similarly, we generate an image r' based on the initial response given by LLaVA- 1.5 for each instance on the POPE (Li et al., 2023d) benchmark. In Figure 3 (Right), we present the density plot of ansen-Shannon (JS) divergence between the predicted probabilities for both images, i.e. po (3e (,, x, yc)) and pe (ye (u', x. yc)), for hallucinatory and non-hallucinatory tokens. The re- sults show that the density of JS divergence follows a long-tail distribution, with hallucinatory tokens exhibiting significantly longer tails and higher IS divergence. This shows JS divergence between prob- abilities derived from the original and the generated image corresponds well to hallucinations at the token level. These observations provide insights into the effectiveness of generative models in reflect- ing hallucinations, and motivate us to incorporate the generative feedhack during the decoding process.
3.3 SFL.F-CRRRECTIG DECODING WITH GENERATIVE FEEDBACK
In this section, we focus on effectively utilizing generative feedback during the decoding process to mitigate potential hallucinations. Specifically, we propose a self-correcting decoding approach that leverages generative feedback to confirm or revise the initial response by selectively enhancing or contrasting the logits for each generated token based the measured divergence between the two predicted probability distributions.
Specifically, to predict specific token y/, we utilize LVLMs generate two output distributions, each conditioned on either the original image or the synthesized visual reference v', expressed as:
Po (yklv, c. x, y<t) = Soff fo (ye (u, x, yc),),, po Iu', x, y =of
x, x,yxt))
We define and compute the following distance metric based on Jensen-Shannon (JS) divergence at each timestep to quantify the discrepancy between two next-token probability distributions:
dv, v') Das (po (vilv, x, |l po (velu', xx))))),
1 where DJs (P |1 Q) 2 PKL M) + 2 (Q II M), and M == -P
(4)
Here, DKL represents the Kullback-Leibler (KL) divergence. Note that de(u, E 1] is symmetric metric, providing a fine- grained measure of how closely the two distributions align as the model predicts each subsequent token.
We consider two scenarios based on the token-level generative feedback: (1) If the two predictions aligned and both images agree on specific token prediction, confirm the original prediction as correct, and the auxiliary prediction from the generated image can be combined with the original
Note that POPE benchmark contains yes-or-no questions about object existence. In this experiment, we evaluate only the first response token (i.e., yes no) determine the presence of hallucinations.
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prediction for enhancement (complementary decoding (Woo et al., 2024)). (2) Conversely, if there is a significant discrepancy between the predictions, indicating that the original prediction is likely hallucinatory, we revise the original response by using the generated visual imput contrasting reference to refine the initial next-token prediction (contrastive decoding (Leng et al., 2024)). To implement this, introduce distance threshold ? and develop corresponding decoding approaches as follows:
Sof [folya v X, y Xct) + on Solualo' yet),,, Softmax [(1 + o2) Lo (yr |, x, ycr) - 02 (o xx,, if if, 0') 2
df, <:
1/+ (1()
7.
(5)
where 01 and 0g are hyperparameters that control the influence of the generated visual reference in the final prediction. Note that setting 01 0 or 02 = 0 degrades this process to regular decoding. The final generated token yr sampled from the multinomial distribution with probabilities po (y()).
4 EXPERIMENTS
In this section, we evaluate the effectiveness of our method in mitigating hallucinations in LVLMs across a range of benchmarking scenarios, comparing it with existing state-of-the-art approaches.
4.1 EXPERIMENTAL SETTINGS
Evaluated LVLMs. We evaluate the effectiveness of our method on three state-of-the-art open-source LVLMs: LLaVA-1.5 (Liu et al., 2024c), InstructBLIP (Dai et al., 2023), and Qwen- VL (Bai et al., 2023). Both LLaVA-115 and InstructBLIP utilize Vicuna-7B (Chiang et al., 2023) the language encoder, which is instruction-tune al., 2023) is based the Qwen 7B backbone. Specifically, we implement our approach using weights of the Qwen-VI--Che model.
from LLaMA (Touvron et al., 2023). In contrast, Qwen- VL (Bai
Benchmarks. We conduct extensive experiments six benchmarks: (1) POPE (Li et al., 2023d) is a widely used benchmark for assessing object hallucinations in LVLMs, which tests the models with yes-or-no questions regarding the presence of specific objects, such as, "Is there a {object} in the image?" (2) CHAIR (Rohrbach et al., 2018) evaluates object hallucinations in open- ended captioning tasks. It prompts the LVLMs to describe specific images selected from a random sample of 500 images from the MSCOCO validation set; (3) MMMe-Halluciattoo (Fu et al., 2023) is comprehensive benchmark for LVLMSs consisting of four subsets: existence and count for object-level hallucinations, and position and color for attribute- hallucinations; (4) MMBench (Liu et al., 2024d) serves as comprehensive benchmark designed to assess the multi-modal understanding capabilities of LVLMs across 20 dimensions; (5) MMVP (Tong et al., 2024) collects CLIP-blind pairs and evaluates the fine-graing -ra-grri visual recognition capabilities of LVLMs. consists of 150 image pairs, each accompanied by a binary-option question; (6) LLa VALVA-Benne provides 24 images featuring complex scenes, memes, paintings, and sketches, along with 60 challenging questions.
Baselines. As a simple baseline, include results from regular decoding, where the token is sampled directly from the post-softmax probability distribution. Additionally, we compare the performance of our method with three state-of-the-ard decoding approaches: VCD (Leng et al., 2024), M3ID (Favero et al., 2024), and RITUAL (Woo et al., 2024). For evaluations on the CHAIR (Rohrbach al., 2018) and MME--Hllluciiationn (Fu et al., 2023) benchmark, we further include comparisons with Woodpecker (Yin et al., 2023), HALC (Chen et al., 2024d), DoLa (Chuang et al., 2024) and OPERA (Huang et al., 2024). We report the performance of these baselines based on our re-implementation using their released code bases.
Implementation Details. In our experiments, we adhere to the default query format for the input data used in both L.a VA- 1.5 (Liu et al., 2024c) and InstructBLIP (Dai et al., 2023). Additionally, we set 01 = 3, a2 = 1, and 7 = 1 by default in our decoding process. We follow VCD (Leng et al., 2024) to implement adaptive plausibility constraints (Li et al., 2023c), where we set 8 = 0.1 in open-ended CHAIR benchmark and = 0.25 for other tasks. To ensure the reliability of our results, we conduct MME experiments three times with different initialization seeds and report the mean accuracy along with the standard deviation. All experiments are conducted on a single 48GB NVIDIA RTX 6000 Ada GPU. More implementation details provided in Section B of the Appendix.

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Table Results on POPE (Li al., 2023d) benchmark. Higher (f) accuracy, precision, recall, and F1 indicate better performance. The best results are bolded, and the second-bess are underlined.
LLaVA-1.5
InstructBLIP
Qwen-Vi
Setup
Method
Acc.
Prec.
f
Acc. 1
Prec. f
F1 f
Acc. f
Prec. f
F1
Regular
83.13
81 1494
83.44
83.07
83.02
83.08
87.43
93.56
86.48
VCD
87.00
86.13
87.15
86.23
88.14
85.88
88, 88.80
93.89
88.11
Random
M3ID
87.50
87.38
87.52
86.67
88,09
86.41
89.83
25.44
89.17
RITUAL
88.87
89.23
88.81
88.83
90.48
88.60
89.47
96.32
88.62
Ours
89,03
91.20
88.74
88.83
93.73
87.71
89.73
93.19
89.31
MS-COCO
Regular
81.17
78.28
82.08
77.00
73.82
78.
84,70
88.24
83,96
VCD>
83.10
79.96
83.94
80.07
77.67
80.89
85.13
87.27
84.69
Popular
M3ID
84.30
81.58
84.95
80.97
77.93
81.85
86.27
89.19
85.73
RITUAL
85.83
84.17
86.17
81.97
78.90
82.87
84.57
84.67
Ours
86.63
87.75
86.28
82.73
84.02
82,10 10
86.50
89.87
85.71
Regular
77.43
73.31
79.26
74.60
71.26
76.45
79.83
80.13
79.73
VCD
77.17
72.18
79.47
77.20
74.29
78.49
81.33
80.60
81.55
Adversarial
M3ID
78.23
73.51
80.22
77.47
73.68
79.14
82.03
81.47
82.19
RITUAL
78.80
74.43
80.54
78.73
74.57
80.39
82.80
83.15
82.71
Ours
81.63
80.59
81.94
80.30
80,90
80.11
83.47
84.49
82.98
Regular
81,90
76.63
83.53
80.63
76.82
81.92
86.27
90.66
85.48
VCD
83.83
78.05
85.34
84.20
80.90
85.00
87.87
90.06
87.53
Random
M3ID
84.67
79.25
85.97
85.43
81.77
86.23
88.13
92.06
87.55
RITUAL
85.17
79.79
86.40
87.13
83,92
87.71
87.73
92.49
87.01
Ours
86.93
84,28
87.42
87.40
87.67
87.26
87.90
89.16
87.58
A-OKVQA
Regular
75.07
68.58
78.77
75.17
70.15
77.91
84.60
87.99
83.
VCD
76.63
69.59
80.19
78.63
73.53
80.72
86.23
87.30
86.03
Popular
M3ID
77.80
70.98
80.91
78.80
73.38
81.00
86.50
89.59
85.95
RITUAL
78.83
71.99
81.68
78.73
72.83
81.17
86.36
88.73
86.20
Ours
80.90
75.68
82.66
81.47
78.61
82.35
86.43
90.74
86.52
Regular
67.23
61.56
73.70
69.87
64.54
74.54
76.90
75.59
77.48
VCD
67.40
61.39
74.21
71.00
65.41
75.45
79.13
76.04
80.30
Adversarial
M3ID
68.60
62.22
75.11
70.10
64.28
75.16
79.50
77.54
80.21
RITUAL
68.57
62,26
74.99
70.27
64.15
75.55
80.20
00.08
80.58
Ours
72.70
66.70
76.86
73.93
69.36
76.67
80.75
80.37
80.46
Regular
82.23
76.32
84.03
79.67
76.05
80.99
84,90
89.51
83.96
VCD
83.23
76.73
85.05
82.83
80.16
83.56
85.21
92.05
84.21
Random
M3ID
84,20
78.00
85.77
83.07
80.06
83.87
85.69
93.11
84.67
RITUAL
86.10
80.30
87.31
84.87
82.52
85,39
86.13
93.78
84.81
Ours
87.40
83.51
85.64
85.12
85.95
94.22
Regular
73.47
66.83
77.84
73.33
68.72
76.26
81.33
83.38
80.74
GQA
VCD
72.37
65.27
77.58
76.13
71.10
78.68
81.97
82.82
81.73
Popular
M3ID
73.87
66.70
75.17
69,94
78.04
82.13
84.58
31.48
RITUAL
74.80
67.50
79.15
74.50
69.17
77.61
81.13
81.03
Ours
78.10
71,56
80.98
76.90
73.89
78.27
82,10
86.39
$1.85
Regular
62,43
63,94
73.10
79.03
78.55 78.54
VCD
68.83
62.26
75.43
65.75
75.14
80.87
81.07
80.80
Adversarial
M3ID
68.67
62.16
75.28
71.17
65.79
75.36
81.03
82.93
80.94
RITUAL
68.23
61,75
75.10
70.17
64.76
74.78
81.07
83.29
80.41
Ours
74.07
67.42
78.22
73.63
70.08
75.11
81.13
84.18
80.57
4.2 RESULTS AND DISCUSSIONS
Results on POPE. Table we compare the performance our method against other baselines on the POPE benchmark under three different negative sampling settings, across three datasets. As shown in the table, our method consistently outperforms other decoding methods on three LVLMs, achieving state-of-thh-aar accuracies across all settings, with improvements of to 5.24% in accuracy, 6.33% in precision, and 2.79% in score compared to the second-best approach. This suggests that incorpo- rating generative reference enables the LVLMs to perceive more fine-grained visual details, thereby effectively addressing object hallucinations. Moreover, while most decoding methods tend to be overconfident in their responses, the coffecorecting decoding mechanism in our method makes it more conservative in responding Yes, as evidenced by significantly higher precision across all settings. This highlights its enhanced performance in filtering false positives and suppressing misinformation.
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Table Results on CHAIR (Rohrbach al., 2018) benchmark. We limit the maximum number of new tokens to 64. Lower (4) CHAIRs, CHAIR, and higher (1) recall and length indicate better performance. The best results in each setting are bolded, and the second-best are underlined.
LL
InstrucIBLIP
Method
CHAIRs 4
CHAIR,
Recall
Length
CHAIRs 4
CHAIR,
Recall
Length
Regular
26.2
9.4
58.5
53.4
31.2
11.1
59.0
53.6
VCD
24.4
7.9
63.3
54.2
30.0
10.1
61.8
54.2
M3ID
21.4
6.3
64.4
53.5
30.8
10.4
62.6
53.4
RITUAL
22.4
6.9
63.0
54.9
26.6
8.9
63.4
55.3
Woodpecker
24.9
7.5
60.8
49.7
31.2
10.8
62.3
51.3
HALC
21.7
7.1
63.4
53.4
24.5
8.0
63.8
55.1
Ours
18.4
6.1
62.7
54.1
24.0
7.7
67.2
55.5
Table 3: Results on Halluuaaluinnttion (Fu et al., 2023) and MMBench (Liu al., 2024d)
benchmark. We report the average MME scores along with the standard deviation across three random seeds for each subset. We also report the overall accuracy achieved by the different methods on the MMBench benchmark in the final column. Higher scores (f) indicate better performance. The best results are bolded, and the second-best are underlined.
Object-level
Attribute-level
Method
Score
MMBench
Existence
Count f
Position f
Color
Regular
173.75 (14. 79)
121.67 ($12.47)
117,92 113 69
149.17 (17.51)
562.50 (13.99))
64.1
DoLa
176.67 (42 8m)
113.33 ($10.43)
90.55 (28.22)
141.67
(#7.64)
522.22 (+16.78)
63.8
OPERA
183.33 (26
137.22 (24.31)
122.78
(2:2
155.00 c25.00)
598.33 (110.41)
64.4
VCD
186.67 (46.77)
125.56
(=3.47)
128.89
(+6.73)
139.45 (=12.61)
580.56 (+15.13)
64.6
M3ID
186.67 (+5.77)
128.33
(0:40.1)
131.67
(#5.000
151.67 (=:20.66)
598.
(+:20.35)
64.4
RITUAL
187.50 (#-2. 88)
139.58
(#77.64)
125.00
(4:10.27)
164.17 (46687))
616.25 (4:20.38)
63.8
Woodpeeker
187.50 (4-2.88 88)
125.00 (=0.00)
126.66
(+2.89))
149.17 (=17.84)
588.33
(+ (+-10.00)
64.0
HALC
183.33 11:0 00)
133.33
(25.777)
107.92
L13 60)
155.00
579.58 (1:9.07)
64.2
Ours
188.33 (::2. 88)
150.00 (27.64)
133.89 (233.8)
172.22
(b:347))
644.44 (29.
65.5
Another notable finding is that our method shows significantly improved performance in the popular and adversarial settings, which are more challenging than the random setting. In the popular and adversarial settings, non-existent negative objects frequently appear and co-occur with other objects (Li et al., 2023d), making them more susceptible to hallucination by LVLMs, as evidenced by the varying degrees of performance degradation across all baselines. However, our method exhibits lower performance drop compared other baselines, demonstrating its effectiveness addressing hallucinations arising from object co-occurrence.
Results on CHAIR. We also compare the performance of our methods and other state-of state-of-the-ate the-har methods in the open-ended captioning task and report the CHAIR scores, recall, and the average length of responses Table 2, Table CI, and Table C2. The results, evaluated across two different LVLMs, consistently demonstrate performance improvements achieved by method over the compared approaches. Specifically, our method outperforms the second-bec -estt approach by 3.0% and 2.6% the CHAIR, metric, while also enhancing the detailedness of generated responses compared to regular decoding, as indicated by the higher recall and increased response length. These results demonstrate that by incorporating generative feedback into the decoding process of LVLMs, our method effectively mitigates object hallucinations in open-ended captioning tasks.
Hall---alluiinoiion and MMBench. Beyond object hallucinations, we further com- pare the performance of our method with other approaches using the more comprehensive MME- Hallucination benchmark, which includes both object-level and attribute-level hallucinations. The results in Table and Table C3 demonstrate that our method significantly outperforms the compared methods, with substantial margins in the total score metric (e.g., +18.19 on LLLVA-115 and +21.11 on InstructBLIP) and consistently superior performance across various evaluation settings, achieving the best results 6 out of 8 settings. Moreover, our method shows notable improvements on the attribute- level color subset, which is particularly challenging as it requires models to accurately capture subtle attribute information. This further illustrates the effectiveness of our approach in addressing a wide range of ballucinations, both at the object existence level and in finer-grained attribute recognition. Additionally, our proposed DeGF enhances the general multi-modal understanding capabilities of LVL.Ms, evidenced by its superior performance on the MMBench benchmark.
Results
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N
12.87
L.L
InstructBI.IP
Method
Acc. f
Det. f
Acc. f
Det. f
Bard
19,00
Random Gueex
Regular
2.88
3.29
3.42
3.96
Ours
4.29
4.54
4.38
4.79
LLwk
22,67
VCD
3.62
3.83
3.71
4.21
K3D
23:33
Ours
4.04
4.38
4.17
4.58
RITUAL
23.83
M3ID
3.88
4.08
4.00
4.33
27:33
Ours
4.04
4.29
4.08
4.50
10
25
MMVP Anturncy (%)
Figure 4: Results MMVP (Tong et al., 2024). We apply our approach to LLaVA-1.5 (Liu et al., 2024c) and compare its performance against other hallucination mitigation methods.
Table 4: GPT-4V-aided evaluation on LLa VA- Bench. Higher accuracy and detailedness (t) in- dicate better performance. The evaluation is per-
formed on LLaVA-1.5 (Liu et al., 2024c).
Visual Input:
[Roguaar Dacoding]
GPT-AV-Auded Evaluator: Accuracy: Derailedness:
This image portrays strategically placed tropical punctured road branching out through giving the appearance of mountain. The
mountain appears to beaurful waters, making picturesque view. the coast bay, there are several houses and buticings, some stouased beach near the water The overall skyline captures a cohesive and captivating ialand landscape. closer at the cloudy sky, which creates serene and natural atmosphere for the charming scene.
GPT-AV-Aided Evaluation: Accuracy: Detailedness: The image displays bird's eye of island with large mountain the center: The mountain steep and appears valcano, with greanery surrounding The island located near running along the bottom the mountain, surrounding area filled with indicating island inhabited. island surrounded by mountairs, providing picturesque and serene landscape. combination octan, mountain, and the houses creases a unique and enwironment.
[Our Approach]
and has road
Query: Describe this photo in decail.
Figure 5: Case study on the -LaVe-Bench benchmark. We compare the responses generated by regular decoding and our method using VL.1..,.5. GPT-4V-aided evaluation results are also provided alongside the responses. Hallucinated and accurate content is highlighted in red and green.
We conduct experiments on the MMVP benchmark to assess the fine-grained visual recognition capabilities of LVLMs. As shown in Figure 4, applying our self-correcting decoding approach to LLaVA-1.5 significantly improves performance from 22.67% to 27.33%. Our approach also demonstrates notable advantages over other hallucination mitigation baselines, further showcasing its superiority in handling nuanced visual recognition tasks. These results suggest that our approach significantly enhances the model's 's capacity to discern and correctly interpret fine- grained distinctions between images with similar appearances but different contents. By integrating generative feedback, our approach effectively reduces misinterpretations and improves the precision of visual recognition tasks, contributing to more reliable and accurate performance in complex scenarios.
Results on MMVP.
Results on LLaVA-Bench. In Figure 5, we present case study on -LaVA-Bench comparing our method's response with the response generated by regular decoding using the LLaVA-1.5 model, Specifically,, regular decoding often leads to hallucinated or inaccurate content, such as describ- ing "the island below the mountain". Besides, the response generated by regular decoding tends focus on elements like the "cloudy sky" and "cohesive and captivating island landscape" without providing specific information about the central features of the image. In con- trast, our response more detailed, mentioning the volcano, the road, the surrounding greenery, and the inhabited areas, which gives clearer understanding of the image's content. The GPT-4V-aided evaluation shown in Table further confirms that our method enhances both the accuracy and detailed- ness of the generated response, outperforming other hallucination mitigation approaches such as VCD and M3ID. Due to the page limit, please refer to Section D of the Appendix for more case studies.
4.3 ABLATION STUDIES
Analysis of Distance Threshold y. In Section 3.3, we introduce distance threshold y to determine the appropriate decoding algorithm for each generated token. Table presents an analysis of our method's performance with various values of y across three benchmarks. For simplicity, we report

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Published as a conference paper at ICLR 2025
Table Sensitivity analysis of distance thresh- old We present the performance of our ap- proach, based on the LLa LLV backbone, across three benchmarks for varying values of 7.
Table 6:
Effects of different generative models. We report the performance of different variants of our method, utilizing various stable diffusion models, on the LLa 1.5 backbone.
Values y
POPE Acc.
CHAIR, CHAIRs
MME Score
Models
POPE Ace.
CHAIRs
CHAIR;
MME Score
7=
87.93
21.0
7.2
622.50
Regular
83.13
26.2
9.4
562.50
0.01
88.07
21.0
6.8
632.22
SD-v1.1
88.37
19.3
6.5
638.33
0.05
88.67
19.4
6.3
637,50
SD-vL.5
89.03
18.4
6.1
644.44
0.1
89.03
18.4
6,1
644.44
SD-v2.1
88.70
18.8
6.7
632.22
?= 0.5
88.73
19.8
6.4
646.67
SD-XL-vOO.9
88.87
18.6
6.1
642.50
7=
88.43
21.6
638.33
SD-XI--v100
88.60
17.9
5.8
648.33
the performance on the MS-COCO dataset with random setting for all POPE results in the ablation studies. Notably, when is set to either 0 or 1 c--orrssponddng to the exclusive use of contrastive or complementary decoding for all tokens the performance exhibits a significant decline, by 0.6% and 1.1% in POPE accuracy, respectively. Moreover, our default setting of y = 0.1 achieves the optimal performance in 3 out of evaluated metries. Additional sensitivity analyses for other hyperparameters provided in Section C of the Appendix.
Effects of Different Generative Models. Table 6 presents the performance of various variants of method that incorporate different generative models (i.e., different versions of Stable Diffusion) while using the same LLaVA-1.5 backbone. The results indicate that the effectiveness of our DeGF is robust the choice of generative models, as performance remains largely unaffected by the specific model used, and all variants demonstrate consistent improvements over the original regular decoding approach. Although utilizing SD-XL-vI (Podell al., 2024) yields slightly better performance, we opt for SD-v1.5 as the default due to its faster image generation speed (3.8 s/image vs. 11.3 s/image).
4.4 EFFICIENCY COMPARISON
In Table 7, we compare the efficiency of our approach with other methods on the CHAIR benchmark using the LLaVA-1.5 model, with the maximum token length set to 128. Our ap- proach involves two queries and incorporates a text-to-image generative model to mitigate hal- lucinations, resulting in a 4.04x increase in la- tency and 1.21x increase in GPU memory usage. Specifically, our method consists of three stages: initial response generation, image gener- ation, and response ceff-correction, which take an average of 3.4 seconds, 3.8 seconds, and 6.6 seconds per instance, respectively. Compared to other approaches, while our method is slower than regular decoding and contrastive decoding-basee methods, demonstrates efficiency advantages over OPERA and HALC. Note that our approach also achieves the lowest hallucination rates among all compared methods. In Appendix C.9, we discuss several strategies to accelerate our approach, such as limiting the length of the initial response and reducing the number of inference steps in the diffusion process.
Table 7: Efficiency comparison. For each method, we present the average inference latency per in- stance and peak GPU memory. Experiments are conducted on single RTX A6000 Ada GPU.
Method
Avg. Latency
CPU Memory 1
CHAIRs
Regular
coton
15778 MB 60000
55.0
VCD
6.91
16634 MB
54.4
OPERA
24.70 s
22706 MR (x
$2.6
Wondpecker
10.68 cuccim
22199 MB
57.6
HALC
22.61 cooon
23084 MR (x) mg
$1.0
Ours
13.89
19119 MB
48.8
CONCLUSION
In this work, we present self-correcting Decoding with Generative Feedback (DeGF), novel training- free approach that leverages feedback from text-to-image generative models to recursively improve the accuracy of generated responses. Specifically, we generate new image based on the initial response given by LVL.Ms, which serves as visual reference and provides token-level feedback for mitigating hallucinations. Building on this, we propose a corresponding self-correcting decoding algorithm that measures the discrepancy between next-token predictions conditioned on the original and generated images, selecting either contrastive or complementary decoding to reduce the likelihood of hallucinatory responses. Extensive experimental results across six benchmarks demonstrate that our DeGF consistently outperforms state-of of-the-ar methods in mitigating hallucinations in LVLMs.
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ACKNOWLEDGEMENTS
This work has been funded part by the Army Research Laboratory (ARL) award W91 23-22-2- 0007 and W91 10X-24-F--044, DARPA award FA8750-23-2-1015, and ONR award N00014-23- 1-2840. MM and LPM are partially supported by Meta and National Institutes of Health awards R01MH125740, R0IMH132225, and M27671 RS supported part by the ONR grant N00014-23-1-2368.
ETHICS STATEMENT
Our work focuses on enhancing the reliability of current large vision- vision-language models. Our research does not involve human subjects, sensitive data, any practices that pose privacy security concerns. We also discuss the broader ethical and societal implications of this work Appendix A.
REPRODUCIBILITY STATEMENT
The large vision-language models utilized our experiments, such as LLaVA and InstructBIIP open-source and publicly available. We have detailed our experimental setup, including hy- perparameter configurations, prompts, and other key design choices, in Section of the main paper and Section B of the Appendix ensure reproducibility. Code is publicly available at https://github com/ /at///aeenee/De......
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SELF-CORRECTING DECODING WITH GENERATIVE FEEDBACK FOR MITIGATING HALLUCINATIONS IN LARGE VISION-LANGUAGE MODELS
APPENDIX
In this supplementary document, we provide additional details and experimental results to enhance understanding and insights into method. This supplementary document is organized follows:
The limitations and broader impacts of this work are discussed in Section A.
Additional experimental details, including further implementation details, descriptions of other implemented baselines, and license information for the utilized code and datasets, provided in Section B.
Additional experimental results are presented in Section C.
More case studies and GPT-4V-aided evaluations are provided in Section D.
Potential directions for future work are discussed in Section E.
A LIMITATIONS AND BROADER IMPACTS
Limitations. Although our method effectively mitigates hallucinations in LVLMs, it relies on pre- trained text-to-image generative models, which introduces additional computational complexity. The process generating images also adds time, potentially slowing down LVLM response generation and making it less suitable for real-time applications. However, our method is training-free reducing the overhead typically associated with fine-tuning large models, and offering broader applicability across various tasks. Moreover, the use of generative feedback improves the model's ability to verify and correct responses, particularly in complex scenarios. Thus, while the computational trade-offs may limit real-time performance, our method excels in settings where accuracy and reliability are prioritized over speed. We also hope that advances in efficient diffusion liffusion-based models will improve the feasibility of our approach in real-world applications in the future.
Broader Impacts. In this work, our goal is to develop more reliable large vision-language models (LVLMs) by incorporating feedback from generative models. By using this feedback mechanism, aim to address a critical issue faced by current multi-modal models: hallucinations, where models produce responses that inconsistent with the visual input. Hallucinations not only degrade model performance but also pose risks in real-world applications by generating inaccurate or misleading information. Our approach leverages the strengths of generative models detect and mitigate these hallucinations, improving the overall accuracy and reliability of LVLMs. In doing so, we contribute to enhancing trustworthiness and reducing the spread of misinformation in systems that rely on multi-modal AI, making them safer and more effective for a wide range of applications.
B MORE EXPERIMENTAL DETAILS
B. BENCHMARKS AND METRICS
We conduct extensive experiments on the following benchmarks:
POPE (Li al., 2023d) is a widely used benchmark for assessing object hallucinations in LVL.Ms. It tests the models with yes-or-no questions regarding the presence of specific objects, such as, "Is there a {object} in the image?" The benchmark draws data from three existing datasets: MSCOCO (Lin et al., 2014), A-OKVQA (Schwenk et al., 2022), and GQA (Hudson & Manning, 2019), and comprises three distinct subsets- -random, popular, and adversarial based on how the negative samples
generated. For each dataset setting, the benchmark provides 6 questions per image, resulting in 3,000 test instances. We evaluate the performance of different methods using four metrics: accuracy, precision, recall, and F1 score.
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CHAIR (Rohrbach et al., 2018) evaluates object hallucinations in open open-ended captioning tasks. It prompts the LVLMs to describe specific images selected from a random sample of 500 images from the MSCOCO validation set and assesses performance based on two metries:
# hallucinated objects
# sentences with hallucinated object
CHAIR, # all objects mentioned'
CHAIRg
# all sentences
Additionally, we assess the recall and the average length of the generated responses.
MMEEHalllucinaiin (Fu et al., 2023) a comprehensive benchmark for LVLMs consisting of four subsets: existence and count for object-level hallucinations, and position and color for attribute-level hallucinations. Each subset includes 30 images and 60 questions, with two questions per image. Similar to POPE (Li et al., 2023d), these questions are structured yes-or-no queries, and performance is assessed based on binary accuracy. Following the official implementation, the reported score is calculated by combining accuracy and accuracy4, where accuracy is based on individual questions, and accuracy+ is based on images where both questions are answered correctly,
MMBench (Liu et al., 2024d) is a comprehensive evaluation benchmark designed to assess the multimodal understanding and reasoning capabilities of AI models. It focuses on tasks requiring the integration of visual and textual information, testing a model's ability to handle diverse, real-world scenarios. In particular, MMBench employs a hierarchical ability taxonomy, designating Perception and Reasoning as Level-1 (L-1) abilities. It further refines the taxonomy by incorporating more detailed ability dimensions, organizing them into six Level-2 (L-2) and twenty Level-3 (L-3) dimensions.
MMVP (Tong et al., 2024) collects CLIP-blind pairs and evaluates the fine-grained visual recognition capabilities of LVLMs. It consists of 150 image pairs, each accompanied by binary-option question. Each image is queried independently, and for a given pair, the LVLM's response is considered correct only if both associated questions answered accurately.
LLa VAL "Bench provides 24 images featuring complex scenes, memes, paintings, and sketches, along with 60 challenging questions. We select examples from this dataset to provide qualitative comparisons between the responses generated by differrnnt decoding methods. We also follow Yin et al. (2023) to evaluate the accuracy and detailedness of generated responses of different methods using the advanced LVLM, GPT-4V4
B.2 MORE IMPLEMENTATION DETAILS
In our experiments, we adhere to the default query format for the input data used in both LLaVA- 1.5 (Liu et al., 2023) and InstructBLIP (Dai et al., 2023). Additionally, we set 01 a2 and y = 0.1 by default in our decoding process. We follow VCD (Leng et al., 2024) to implement adaptive plausibility constraints (Li et al., 2023c):
(3)) = 0, if Ve & V(Bc()
where V(yet) = {ye E V: po (1 x,y 2 B max (wwl,, x,yc()
(7)
ve
Here, V is the whole vocabulary of LVLM, and hyperparameter 3 [0. 1] controls the truncation of the next token distribution. A larger 3 indicates more aggressive truncation, keeping only the gighhprobability tokens. In our implementation, we set the logits for y & V(y<r) to -cc. By default, we set B 0.1 in the open-ended CHAIR benchmark and 3 = 0.25 for other tasks. All experiments are conducted on a single 48GB NVIDIA RTX 6000 Ada GPU.
Recall that in our method, we use text-to-image generative model to reverse the image-to-ter text response generation process by producing a new image from the initial response. To ensure the new image is both high-quality and relevant, we aim to generate specific descriptions for the given visual content. Thus, we slightly modify the initial query prompt for each evaluated benchmark:
POPE (Li et al., 2023d), AMME-Hallccinatoon (Fu et al., 2023), and MMVP (Tong et al., 2024). POPE, AMME-Halluinnttion and MMVP benchmarks, models are tested with nob-aryy selection questions, such as, "Is there {object} in the image?" ? To obtain more detailed explanations and descriptions of the original image, we modify the prompt by adding, "Briefly
"nttpe://huggingface co/ httpa///cc.e//ttttttttttaaataaaa httppe///opna...cc////////g///ae wtt.coonn/ccieeeeceecc gpt sysstemm------------------------ cardd.
bench- in-tinn-eee the-wild.
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describe relevant details. This encourages the model to provide not only yes-or-no answer but also additional visual information.
CHAIR (Rohrbach al., 2018). For the CHAIR benchmark, we retain the original prompt, "Please describe this image in detail." " as it effectively prompts the model to provide comprehensive visual details from the original image.
Note that for the second query, where both the original and generated images are used as input, we apply the original prompt to ensure a fair comparison.
B.3 DETAILS OF OTHER BASELINES
In this work, we mainly compare the performance of DeGF with three state-of-the-art approaches: VCD (Leng et al., 2024), M3ID (Favero et al., 2024), and RITUAL (Woo et al., 2024). The method and implementation details for these approaches are provided below:
VCD (Leng et al., 2024) contrasts output distributions derived from original and distorted visual inputs. Specifically, given a textual query æ and a visual input v, the model generates distinct output distributions: one conditioned on the original and the other on the distorted visual input v', which is derived by applying pre-defined distortions (i.c., Gaussian noise mask) Then, new contrastive probability distribution is computed by:
Poed (vx) Softmax [(1 + a (ylv, x,yc)) - a (ylv', (,lu',x,y<x)) x,y=t)]
(8)
In our implementation, follow the default setting in VCD (Leng al., 2024) and a 1 for reproduction. To generate v', we use a total of 500 noise steps.
M3ID (Favero et al., 2024) contrasts output distributions derived from original visual inputs and pure text inputs without visual information. The final probability distribution is
1 e-
Pmaid (yr) Softmax So (glt,
So
(So (ylc, So (olx. (9)
Similarly, we follow their recommended best practice and set the hyperparameter X, which balances the conditioned model and unconditioned model, to 0.02.
RITUAL (Woo et al., 2024) applies common image transformations (e.g., crop, flip, color jitter, etc.) to the original visual input v, This results in transformed version of the visual input, o(T), Then, RITUAL utilizes both the original and transformed images to generate the response and this dual-input approach significantly reduces the likelihood of hallucinatory outputs. The probability distribution is calculated as follows:
Pritual (we) Softmax [To ycl) + HNfe (ylu(T) xxy<()]
(10)
Here, i. is a balancing hyperparameter, adjusting the contribution of the transformed input relative the original. We follow their official implementation to set in default.
B.4 DATASET AND CODE LICENSING
Datasets. We list the known license information for the datasets below: POPE (Li et al., 2023d) and MMVP (Tong et al., 2024) benchmarks are licensed under MIT License. CHAIR (Rohrbach et al., 2018)ii is made available under the BSD 2- 2-Clause License. LLaVA- -irA--Bechhh is available under Apache- 2.0 License. AMME-Hallccimg University for academic research only.
(Fu et al., 2023) benchmark dataset collected by Xiamen
Code. In this work, we also use some code implementations from the existing codebase: LLaVA (Liu et al., 2023) and VCD (Leng et al., 2024) are licensed under the Apache-2.0 License. Instruct- BLIP (Dai et al., 2023) is under BSD-3-Clause License. RITUAL (Woo et al., 2024) is licensed under MIT License.
C MORE EXPERIMENTAL RESULTS AND ANALYSIS
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Table Cl: Results on CHAIR (Rohrbach al., 2018) benchmark. We limit the maximum number of new tokens to 128. Lower (4) 5 CHAIR, and higher (1) recall and length indicate better performance. The best results each setting are bolded, and the second-best are underlined.
L.
InstrucIBLIP
Method
CHAIRs +
CHAIR,
Recall
Length
CHAIRs 4
CHAIR,
Recall
Length
Regular
55.0
16.3
71.9
97.3
$7.0
17.6
68.3
100.4
VCD
54.4
16.6
75.1
97.0
60.4
17.8
72.5
99.9
M3ID
56.6
15.7
76.8
94.5
62.2
18.1
71.9
99.8
RITUAL
49.6
14.8
74.7
96.2
48.4
14.5
72.2
100.0
Woodpecker
57.6
16.7
70.3
93.2
60.8
17.6
69.7
97.6
HALC
51.0
14.8
75.3
95.8
53.8
15.7
71.9
99.1
Ours
48.8
14.6
76.0
96.4
49.2
14.4
72.2
98.9
Table C2: Results on CHAIR (Rohrbach al., 2018) benchmark. We limit the maximum number of new tokens to 256. Lower (4) CHAIRs, CHAIR, and higher (1) recall and length indicate better performance. The best results each setting are bolded, and the second-best are underlined.
LLaVA-1.5
InstructBLIP
Method
CHAIR 4
CHAIR,
Recall
Length
CHAIRs
CHAIR,
Recall
Length
Regular
58.0
17.7
74.1
106.3
61.0
18.2
68.9
112.0
VCD
58.2
16.7
78.0
103.5
63.0
18.6
72.9
106.3
M3ID
56.8
16.1
80.7
98.2
65.8
19.9
72.4
102.7
RITUAL
51.0
15.1
76.0
100.9
50.4
72.0
102.0
Ours
49.8
14.7
77.2
103.3
49.8
15.1
72.3
103.3
Table C3: Results on MMME-Hallcinaate (Fu et al., 2023) benchmark. We report the average MME scores along with the standard deviation across three random seeds for each subset. We also report the total scores achieved by the different methods across all four subsets in the final column. Higher scores (1) indicate better performance. The best results are bolded, and the second-best are underlined.
Object-level
Attribute-level
Model
Method
Total Score 1
Existence
Count
Position
Color
Regular
173.75 cb4.79)
121.67
(212.77)
117.92 (23.69)
149.17
(27.51)
562.50 (23.90)
DoLa
176.67 (+2.89)
113.33 (=10.41)
90.55 (#8.22)
141.67
(=7.64)
522.22
(+ (+16..7)
OPERA
183.33 (16.48)
137.22 (16.0)
122.78 (12.55)
155.00
:2: 00)
598.33
(1:0041)
LLaVA-1.5
VCD
186.67 (15.77
125.56
(13.47)
128.89
(1v.773)
139.45
(1)
$80.56
(1:15.10)
M3ID
186.67 (t5.77)
128.33
(210.4))
131.67 (25.00)
151.67
(220.88) 98)
598.11
(1:00.5))
RITUAL
187.50 (#-2.89)
139.58 ($7.64)
125.00
(#:10.27
164.17 (66.87)
616.25
(+220.35)
Ours
188.33 (1:2.89)
150,00 (17.664)
133.89
172.22
644,44
Regular
160.42 16)
79.17
(2:.22) 22)
79,58 (d:8.54)
130.42 (t: 17.54)
449.58
(+:44.0)
DoLa
175.00 (4:5.00)
55,00 (4.5.00)
48.89 (4-3.47)
113.33 (1:6.67)
392,22 (#:7.88)
OPERA
175.00 (1:333)
61.11 (2:3.47)
53.89 (1:1.92)
120.55
410.56 (2/9.07)
InstructBLII
VCD
158.89 (#5.85)
91.67 (4:18.34)
66.11 (+9.76)
121.67 (#.12.5)
438.33 (6:16.07)
M3ID
160.00 ct5.00)
87.
(19.188)
125.00 (=7.64)
4411.67
(d:17.8)
RITUAL
182.50 (1:6.45)
74.58 98)
67.08 (110.31)
139.17 (1:0.90)
463.33
(4:12.40)
Ours
186.67 (1:2.890
89.44
58.33
150.00
484.44
(1:11.50)
Regular
155.00 (t5.54)
127.67 67 (#13.36)
131.67 (#7.77)
173.00 (2:9.7))
587.33
(+:11.6)
VCD
156.00 (1.6.
131.00 (16619)
128.0 (133661)
181.67
(1:5.14)
596.67
(4:11.61)
Qwen-VI.
M3TD
178.33 (1:2.89)
143.33 (12.88)
150.00 (22
175.000 :2:5
646.66
(28.50)
RITUAL
178.33 (+22899)
142.22 (#16.10)
156.66 (+2.89)
178.33
655.55 (414.99))
Ours
180.00 c1.0.00)
148.89
(1.6744
155,00
178.33
662.22
C.I ADDITIONAL RESULTS ON CHAIR
In Table C1 and Table C2, we present performance comparisons on the CHAIR benchmark with maximum number of tokens set to 128 and 256. The results indicate that our approach also achieves competitive performance across two LVLMs in mitigating hallucinations during long-sequence generation scenarios.
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Table C4: Detailed results on MMBench benchmark. Abbreviations adopted: LR for Logical Reasoning; AR for Attribute Reasoning; RR for Relation Reasoning; FP-$ for Fine-grainet Perception (Single Instance); FP-C for Fine-graiee sine-grained Perception (Cross Instance); CP for Coarse Perception. The best results are bolded.
Method
LR
AR
RR
FP-S
FP-C
CP
Overall
Regular
30.51
71.36
52.17
67.58
58.74
76.35
64.09
VCD
30.51
73.37
53.04
67.92
57.34
77.03
64.60
M3ID
30.51
72.36
53.04
67.58
57.34
77.36
64.43
RITUAL
28.81
72.86
54.78
65.87
58.04
76.01
63.83
Ours
31.36
70.85
60.87
68.60
58.74
77.36
65.46
Table C5: Detailed results on MMVet benchmark with regular sampling. Abbreviations adopted: Rec for Recognition, OCR for Optical Character Recognition, Know for Knowledge, Gen for Language Generation, Spat for Spatial Awareness, Math for Mathematics. The best results are bolded, and the second best are underlined.
Method
Rec
OCR
Know
Gen
Spat
Math
Total
Regular
30.8
19.0
14.5
17.9
26.9
11.5
26.1
VCD
35.6
21.9
18.3
21.9
28.9
3.8
30.9
M3ID
35.0
19.7
18.8
19.0
26.0
7.7
29.9
RITUAL
36.3
20.6
19.5
21.1
24.7
7.7
30.6
Ours
35.9
27.2
19.2
22.4
30.4
11.5
33.0
C.2 FULL RESULTS ON HALLUCAICNNNNTION
In Table C3, we present the full results on the MME Hallucination benchmark across three LVLMs.
C.3 FULL RESULTS ON MMBENCH
In Table C4, we present the overall performance on the MMBench benchmark, as well as the detailed performance across six Level-2 abilities: Logical Reasoning (LR), Attribute Reasoning (AR), Relation Reasoning (RR), Fine-grained Perception Single Instance (FP-S), Fine-grained Perception Cross Instance (FP-C), and Coarse Perception (CP). We follow VCD (Leng et al., 2024) to conduct experiments on the MMBench-dev set.
C.4 RESULTS ON MM-VET
In Table C5 and Table C6, we present the overall performance on the MM-Vet (Yu et al., 2024) benchmark with random sampling decoding and greedy decoding strategies, respectively. We use LLaVA-1.5 the LVLM backbone. From the results, we observed that our method consistently outperforms others on the MMVet benchmark. Notably, significantly excels the OCR, spatial awareness, and math subsets.
C.5 RESULTS ON POPE USING GREEDY DECODING
In Table C7. present performance comparisons the POPE benchmark with random sampling from the MS-COCO dataset. The experiment conducted using the L.L. backbone.
C.6 EFFECTS OF AND 02 IN SELF- COTFFCOORECTING DECODING
In Section 3, we present two decoding approaches: complementary decoding and contrastive decoding. We also introduce balancing hyperparameters, on and (:2, which control the relative influence of
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Table C6: Detailed results on MMVet benchmark with greedy decoding. Abbreviations adopted: Rec for Recognition, OCR for Optical Character Recognition, Know for Knowledge, Gen for Language Generation, Spat for Spatial Awareness, Math for Mathematics. The best results are bolded, and the second best are underlined.
Method
Rec
OCR
Know
Gen
Spat
Math
Total
Greedy
37.0
22.6
17.5
20.2
24.9
7.7
31.8
VCD
38.2
22.8
22.5
24.6
25.1
3.8
33.4
M3ID
37.9
23.6
20.4
20.7
26.0
11.5
33.2
RITUAL
35.6
21.7
18.9
19.9
24.7
7.7
30.6
Ours
37.9
25.0
20.2
19.5
32.8
15.0
34.0
Table C7: Results POPE using greedy decoding.
POPE
Values
Acc.
Prec.
Rec.
F1
Greedy
87.73
88.19
87.13
87.66
VCD
87.47
86.64
88.60
87.61
M3ID
89.07
89.54
88.47
89.00
RITUAL
89.23
90.17
88.07
89.11
Ours
89.40
94.44
83.73
88.76
the original and generated images next-token prediction. In Table C8 and Table C9, we analyze the effect of varying di 02 while keeping all other hyperparameters The results indicate that our default choice a = and a₂ = consistently yields the best performance across benchmarks. Moreover, compared setting these hyperparameters to O, which effectively reduces complementary/coonrssiiv decoding to standard decoding, the performance improvements demonstrate that our proposed decoding approaches significantly contribute to the overall effectiveness of DeGF in mitigating hallucinations in LVLMs.
their default settings.
C.7 EFFECT OF IN ADAPTIVE PLAUSIBILITY CONSTRAINT
We further conduct an ablation study on 3 introduced in Equation (7), where we vary B from to 0.5 while keeping all other hyperparameters fixed. The results in Table C10 show that setting = 0, which imposes no constraint, results in suboptimal performance across both benchmarks. Additionally, in the POPE benchmark, where LVLMs handle yes-or-no questions, truncation with 3 = 0.25 yields the best performance. In contrast, for the open-ended CHAIR benchmark, a lower value of B 0.1 leads the best results.
aggressive
C.8 SCALING UP THE LVLMS
We further extend evaluation to larger-scale 13B variants of the LLaVA-115 model to assess the scalability of our approach. Table C1 I compares our experimental results with other state-of-the-art approaches across all three subsets of the POPE benchmark using the 13B-sized LLaVA-1.5 model. We observe that scaling up the LLaVA-1.5 model does not alleviate the hallucination issues, as evidenced by the comparable performance of both the 7B and 13B models. Using the 13B-sized model, our DeGF consistently achieves improved performance across all subsets compared to other approaches, demonstrating its general effectiveness and scalability.
C.9 SPEEDING UP OUR APPROACH
In this section, we propose two strategies to accelerate our approach: limiting the length of the initial response and reducing the number of inference steps in the diffusion process.
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Table C8: Sensitivity analysis of hyperparameter a1. We present the performance of our approach, based on the LLaVA-1.5 backbone, across two benchmarks for varying values of 01. Note that we fix 02 1 in this experiment.
POPE
CHAIR
Values
Acc.
Prec.
Rec.
F1
CHAIRs
CHAIR,
Q1 = 0
87.50
86.71
87.49
87.10
22.8
7.6
Q1 1
87.97
87.28
87.34
87.31
20.6
6.9
Q1 = 2
88.90
89.39
87.75
88.56
19.4
6.3
01 = 3 89.03
91.20
86.40
88.74
18.4
6.1
Q1 = 4
88.67
90.56
85.28
87.84
22.6
8.1
Table C9: Sensitivity analysis of hyperparameter 02- We present the performance of approach, based on the LLaVA-1.5 backbone, across two benchmarks for varying values of 02. Note that we fix 01 3 in this experiment.
POPE
CHAIR
Values
Acc.
Prec.
Rec.
F1
CHAIRs
CHAIR,
Qg2 =
86.77
85.17
86.58
85.87
23.6
8.2
Qg 1
89.03
91.20
86.40
88.74
18.4
6.1
Qg = 2 88.73
89.86
86.66
88.23
21.8
7.5
02 3 88.03
87.97
86.28
87.12
22.8
7.3
Q2 = 4
87.13
86.52
86.16
86.34
23.6
7.9
Reducing Diffusion Inference Steps. By default, we set the number of diffusion inference steps to 50 to ensure high-quality image generation. To improve the response generation speed, we can reduce the number of diffusion steps. In Table C12, we report the performance on the CHAIR benchmark after reducing the diffusion inference steps in the model. By reducing the diffusion inference steps from 50 to 10, the average latency decreases by 2.85 seconds per instance, while the performance CHAIR remains robust. This demonstrates that reducing the inference steps of the diffusion model is an effective way to speed up our approach.
Restricting Length of Initial Response. Our method involves two queries to the LVLM for self-correcting decoding. To enhance efficiency, we can limit the length of the initial response. In Table C13, we present the efficiency and CHAIR performance results after decreasing the maximum token limit for the initial response. We can see that reducing the maximum number of tokens in the initial response from 128 to 96 decreases the latency by 0.72 seconds per instance while maintaining competitive performance. However, further reductions result performance degradation, shorter initial response fails to adequately cover the entire scene, limiting its ability to generate an image that effectively reflects and mitigates hallucinations.
Note that these two strategics diffusion steps to 10 and limiting the maximum number tokens in the initial response to 96 further reduces the inference latency to 10.21 seconds per instance while maintaining robust performance.
not confficting; instead, they complementary. Setting the
1. QUANTITATIVE ASSESSMENT OF GENERATED IMAGE QUALITY
Our approach incorporates a text-to-image generation model to mitigate hallucinations. We evaluate the quality of the generated images on subsets on the MME benchmark using CLIPScore (Hessel al., 2021). 1). Specifically, we utilize the CLIP backbone with VIT-B/32 backbone for our evaluation. We list the results in Table C14. As we can see from the table, our text-to-iman -mag generative model (specifically, SD-v1.5) achieves average CL.PScore of over 30 across all subsets. For comparison,
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Table C10: Sensitivity analysis of hyperparameter B. We present the performance of approach, based on the LLaVA-115 backbone, across two benchmarks for varying values of B.
POPE
CHAIR
Values
Acc.
Prec.
Rec.
F1
CHAIRs
CHAIR;
0
87.17
87.45
85.30
86.36
21.2
7.1
8 0.05
88.27
89.85
86.
87.95
19.1
6.3
_ = 0.1
88.33
89.04
86.04
87.52
18.4
6.1
0.25
89.03
91.20
86.40
88.74
19.3
6.5
0.5
87.80
88.79
85.48
87.10
20.2
6.9
Table C11: Results on POPE (Li et al., 2023d) benchmark using 13B-sized V.L.5 Higher (f) accuracy, precision, recall, and Fl indicate better performance.
Setup
Method
Ace. f
Prec.
Rec.
F1
Regular
82.53
78.57
89.47
83.67
VCD
84.80
80.67
91.53
85.76
Random
M3ID
85.37
81.30
91.87
86.26
RITUAL
87.80
84.45
92.67
88.37
Ours
88.40
88.14
88.61
88.37
MS-COCO
Regular
80.53
76.17
88.87
82.03
VCD
82.23
76.88
92.20
83.84
Popular
M3ID
82.60
77.91
91.00
83.95
RITUAL
84.07
79.00
92.80
85.35
Ours
85.30
84.18
86.93
85.53
Regular
75.80
70.41
89.00
78.62
VCD
77.33
71.44
91.07
80.07
Adversarial
MBID
77.43
71.65
90.80
80.09
RITUAL
78.00
71.72
92.47
80.78
Ours
81.43
78.61
87.04
82.61
the advanced DALL-E 3 model achieves score 32.0, while DALL-E achieves 31.4. These results highlight the capability of our model to generate high-quality images that closely align with the initial response.
D MORE CASE STUDIES
D.1 DETAILS ABOUT GPT-4V-AIDED EVALUATION
Following VCD (Leng et al., 2024), we use GPT-4V to evaluate responses in open-ended generation scenarios, scoring them based on accuracy and detailedness. Leveraging the strong human- -ike capabilities of GPT-4V, it can detect incorrect colors, positions, and relationships, providing comprehensive evaluation the responses. Specifically, we apply the prompt provided in Table D15 to instruct GPT-4V to rate the two responses on scale of to 10 for both accuracy and detailedness:
Accuracy measures the consistency between the responsesddescripioon generated by the LVLMs and the given image. A lower score is assigned if GPT-4V detects any inconsistencies in the content of the responses.
Detailedness evaluates the depth and specificity of the responses provided by the LVLMs. higher is awarded if the response includes comprehensive descriptions, captures fine-grained details
'These results are sourced from the technical report DALL-E.3 3, available at: https://ccae openai. htm/paperrsddall-eeee pdf.
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Table C12: Effect of reducing diffusion infer- ence steps.
Table C13: Effect of restricting the number of
tokens in the initial response.
Diff. Steps Avg. Latency &. CHAIRs & CHAIR, 4
# Tokens
Avg. Latency & CHAIRs 4 CHAIR, +
50
13.89
48.8
14.6
128
13.89 s
48.8
14.6
30
12.56
48.9
14.7
96
13.17
48.8
14.9
20
11.87
64
12.20
49.5
14.8
10
11.04 s
48.8
14.9
32
11.33
51.2
14.9
Table C14: CLIPScore evaluation across different MME subsets.
MME Subset
Existence
Count
Position
Color
Avg. CLIPScore
31.34
30.69
30.09
31.69
of the image, and provides well-elaborated explanations. Conversely, lower score is given if the response vague or lacks sufficient detail.
D.2 MORE QUALITATIVE RESULTS
In Figure D1 and Figure D2, provide additional case studies ILaVA-Bence to qualitatively demonstrate the effectiveness of our methods in mitigating hallucinations. We also included GPT-4V evaluations of accuracy and detailedness scores for each instance.
In Figure D3-D6, we provide qualitative evaluations of the images generated by the generative model, including both success and failure cases, across all four subsets of the MME benchmark better understand the effectiveness of the generative models. Our results show that, despite occasional failure cases, the generative model consistently produces high-quality and realistic images that accurately visualize the initial response, providing effective self-feedback.
E FUTURE WORK
In future work, we aim extend the evaluation of our method broader range LVLMs, such as Mini-GPTH (Zhu et al., 2024) and mPLUG-Ow12 (Ye et al., 2024), as well as additional benchmarks, including R-Bench (Wu al., 2024), which focuses on relation hallucination, 2024c), which addresses multiple-object hallucination. This expanded evaluation will allow to more comprehensively assess the generalizability and effectiveness of our approach across diverse models and tasks.
ROPE (Chen et al.,
Furthermore, we plan to investigate integrating generative feedback directly into the instruction tuming phase. This integration has the potential eliminate the computational overhead associated with applying our method during inference, thereby significantly improving efficiency without compromising performance. By pursuing these directions, hope further enhance the practical applicability and scalability of our approach.
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Description:
Al that scores image description accuracy and detailedness.
Instructions:
You are an Al designed to evaluate and score the performance of two AI assistants in describing given image. Your primary focus is on the accuracy and detailedness of their descriptions. You will assess the accuracy by checking for hallucinations any part of the description that inconsistent with the image content. For detailedness, you will consider how rich response provide scores on scale from to 10 for each assistant separately, based on these criteria. After scoring, you will offer an explanation for your evaluation, ensuring it is free from bias and not influenced by the order of presentation of the responses.
necessary details, excluding any hallucinated parts.
will
Input format:
[Assistant 1] [Response 1} [End of Assistant
[Assistant 2] [Response 2 [End of Assistant
Output format:
Accuracy:
Scores of the two answers:
Reason:
Detailedness:
Scores of the two answers:
Reason:
Table D15: GPT-4V-aided evaluation setup. We present the prompt provided GPT-4V to evaluate the LVLM responses based on accuracy and detailedness.
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Visual Input:
[Regular Decoding]
GPT-4VAAdded Evaluation: Accuracy. 3 Detailedness
The image features table with four black fruit, possibly berries, cut half reveal their white Interiors. The placed table, with the fruits, there other the right side. combination fruits and bonanas creates
side, center, and right addition barara located
side table, while the appetizing dispaay.
GPT-4V-Aided Evaluation: Accuracy: 7 Detailedness surface, possibly a wooden Table.The be seen, with some positiored doserr to the viewer's 's perspective, making them the most noticeable elements of the scene. The other two fruits on the table are partally visible but appear to be obscured by the more central fruits, adding an interesting contrast between the visible and absoured elaments in the image.
Approach]
The image displays an arrangement black and grean fruits
fruits are central focus, covering a large portion the foreground,
these cut
Query: Describe this photo detail.
Visual Input:
[Regular Decoding)
GPT-4WAidedd
Accuracy: Detailedness:
The image painting a dog woaring Bizabethan style ruff collar. it "painted" appearance alongside Pinocchio. The deg is posing for painting, it's commissioned It's sested while holding a book
its paws. In background emphasize the dag's artistic appearance. Around the scene, handbag can be seen positioned near the top-right corner of the painting
painting, another large dog visible, parhaps meant
[Our A Approach)
GPT-AV-ided Evaluation: Accuracy: Detailedness:
The image features a dog wearing a hood and a giving an artistic and mysterious appesrance The dog is positioned in the center of the scene, and its attire appears to be inspired by the famous Mona Lisa painting The hood and gown give the dog regal and enigmatic look, reminiscent of the famous portrait of the Mona
Query: Describe this photo in detail.
Lisa. The image is painted in a black and white style, further emphasicing the artistic nature of the scenee
Visual Input:
GPT-4V-Aided Evalustion: Accuracy: 3 Detailedness: The scene depicts man sitting on a small folding chair, ironing a blue shirt on the back of yellow city cab parked on crowded city street A red car parked in front of the yellow cab, close proximity the overall the forsground.Amont the parked cars, there is teddy bear visible adding playful touch to the urban catching break by doing duty the midst traffic.
[Regular Decoding]
There several other vehicles the street well including truck and a few
surroundings. The
[Our Approach]
GPT-4V-Aiee Evaluation: Accuracy: 4 Detailedness: 5
The image depicts a man riding on the back of yellow taxi while ironing clothes, He is sitting on a portable ironing board, which is strapped to the taxi's back. The truck driving down a busy street, with other vahicles such cars and bus visible in the scene. There are several plants placed along the street, adding some greenery to the urbon environment. The man appears to be er erjoying his unique mode of transportation tation while talóng care of his lauddr..
adreet.
Query: Describe this photo detail.
GPT-4V-Aided Evalustion: Acturacy: 4 Detailednese The image features a well-stocked refrigerator filled with variety of food itemss On its shelves, there are several bottles, some filled and others empry, accupying varying positions and heights. In addition to the bottles, there are fresh fruits like grapes and strawberries disphyed, and multiple carrets arranged in the bottom part of the fridge.Tie The diverse array of items, including some fruits, vegetables, and beverages, showcase an organized and will-tookeed refrigerator with locus on healthy nating options.
[Regular Decoding]
Visual Input:
[Our A Approach]
GPT--ViAided Evaliation: Accuracy: 6 Detailedness
The image displas a well-stocked refrige refrigerator filled with a variety of food items. There are several carrots placed different compar tementtttssssss with some in plattic container and others in a green trey.The refrigerator also contains milk,, yogurt, joguut,juicc,, and strawberries, nearly arranged to create an organized and visually appealing disply. in advition to the food items, there are a few howls placed within the refrigerator, possibly for holding leftovers or additional ingredients. The refrigerator is filled with a diverse assortment of foods.
Query: Describe this photo in detail.
showcasing a well-stocked and wall-orgentzed lotchen.
Figure DI: Case studies on the LLa Bench benchmark. We compare the responses generated by regular decoding and our method using LLaVA-1.5. GPT-4V-aided evaluation results are also provided alongside the responses. Hallucinated and accurate content is highlighted in red and green.
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Visual Input
GPT-4VAidee Evalustion: Accuracy. 3 Derailedness: In this image, there a pan placed on the stovetop topped with multple pieces of golden fried chicken. The cooked chicken pieces are scattered around the pan, creating a circle formation around it. The arrangemens of the chicken pieces and their postioning over the pan give the impression of the Earth as if the pieces of chicken are flying the sky or travcling through space. This creative presontazion gives the appearance of global exposure "I marveled how beautiful all is.
[Regular Decoding]
Sometimen
with fromappen see.appeddedidd of how Lo aill i.
The image features frned chicken party the shape GPT-VVAided Evaluntion: Accuracy.7 7 Decailedees:: map, placed on a mecal baking sheet.
Approach]
chicken party is into several all places, resombiing different countria around globo. chickan piaces are arranged to represent various countries, with some pieces being lorger and more prominent than others. The unique presentation of the chicken as a world map makes it an interesting and creative dihh.
Query: Can you explsin mome detail?
Visual Input:
GPT-4V-Aided Evaluation: Accuracy:2 2 Detailedness: The image a comic strip featuring a man with a stack heads his chest, symbolizing a stack leyers and several points on his fare. He is talkiing about stacking lyyers. The comic plaeed next to smaller version of the same sceme, and both scenes appear to be related to the topic of neural networks ks lyyord systeme. addition to the man with the stack beach,, there snother pop churacter,john john McClame himsell, whose face is also incorporated into the soene, making humorous connection to the main concept.
[Reguler Decoding]
Accuracy: Detailedness: The image consists of two comics depicting a person teaching startittis, In the first comic, the person is shown pointing to a graph, while in the second comic, they are pointing to a different graph. The person is explaining layons, stack more layers, and neural networks to the audience. The image is filled with various graphs and diagrams, which are used to Illustrate the concepts being dicussed. The person appears to be angry while teaching. emphastaing the importance of the topic. The comic a mix of serious and humorous elements, making it an engaging and informative visual.
[Our Approach)
GPT-4VAided Evoluation:
STACK MORE LAYERS
Racimmmullly Query: detailed description of mome.
Visual Input:
APdd4V-Auded Evaluation: Accuracy: 2 Detailedness The LLMAA logo features large eystoll, possibly a gonfy looking onn, with whrant orange and red flrmes licking upward around its borders. The overall design appears be quite intricate and abstract. choice colors unconnentional design might be Inspired by an arrusing parody or a playful take on artifiial incelligance 2001:A Space Odyascy The stark contrast botweon conty composed "eye" and the flames gives logo unique aesthetic, sparking curlosity and sttention while possibly conveying the concept aroiiiial Incellgence. of humor or creative unsentlement may effectively communicate underlying message lure more people to appreciate the idea behind LLaVA.
[Regular Decoding)
HAL 9000 reforence
viewer,
[Our Approach]
GPT-4VAArld Evakarion: Accuracy: 8 Detailednnees
The LL-VA lugo is playful and unique design featuring Tre-brewthing toy cameel The camel standing on two black fest, flames coming out mouth The glassss on camal's face touch whinsy the design. The overall appoarance the lago is visually engaging and memorable, y aiming to represent the innovative creative nature LLaMA architecture design of using a igure an unconventional and eye-catching element, wirch can logo stand impr essio viewers.
Query: the logo LLAVA, Large Language
and Viston Asststant, based on the LLaMA architecture, Please explain this logo detail and how you think design.
central
strong
Visual Input
GPT-4V-Aided Evalustion: 2ccuracy:2 Decalledness: The scene dispays delicious meal with a bowl of soup, bowl gyro, and another bowl containing greed. There's also fried egg
[Rogular Decoding]
bowl offering tompting mix of colors and textures. spoon placed near soup, ready for when enjoying this mouthwatering assort asortment The various foods and bowls present enticin boatt libely appealing those with diverse appatita.
[Our Approach]
GPT-AWAided Evaluntion: Accuracy: Decailedness: 5
The image features dining table with large bawl of delicious noodles, meat, and vegetables. The bowl filled with various ingrodients, such as ontons, carrots, and green peppers, makin appetizing meal. In addition to the main bawl, there are soveral smaller bowls placod on the table, containing different food Icemss A spoon also visible on the table, lialy for serving the noodle The arrangement of the bowls and the spoon suggests that this meal shared among friends or lamil.
Query: Describe this photo in detail.
Figure D2: Case studies on the -eBnhh benchmark. We compare the responses generated by regular decoding and our method using LLaVA-1.5. GPT-4V-aided evaluation results are also provided alongside the responses. Hallucinated and accurate content is highlighted in red and green.
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Query: Is there tie in this image! Describe relevant details about this.
Initial Response
Yes, there is large, four-tiered wedding cake in the image, and it adorned after being done. The cake features unique details like pink flowers. It stands on top of a white cake plate, appearance is perfect for special event, such wedding.
overall
Correct Generation
Query: there a kite this image! Describe relevant details about this.
Initial Response
Yes, there kite that
the image. It being flown by someone in an open yard near the contrast
hydrant. This scene serves between the urban setting with the fire hydrant and pole, the more recreational activity taking place the kite flying.
Correct Generation
Query: Is there car in this image? Describe relevant details about this.
Initial Response
Yes, there car in the image. There a car on the street, which is parked next to some buildings and traffic lights. The car appears to be lining up enter business area behind man standing near street crossing sign.
Correct Generation
Query: there skateboard in this image? Describe relevant details about this.
Initial Response
Yes, there skateboard this image. A man is jumping mid-air the skateboard with his hands and feet out. The skateboard and the it in motion, which adds to the excitement and action of the scene.
Correct Generation
Query:
there mouse in this image? Describe relevant details about this.
Initial Response
There mouse the image, but it appears be cell phone rather than regular computer mouse. placed together with phone and other items, such as paper, glass, and possibly keyboard, on desk.
X Incorrect Generation Figure D3: Qualitative evaluation of images generated by the generative model on the existence subset of the MME benchmark. Specifically, the left displays the original image input, the middle presents the initial response generated by the LVLMs, and the right shows the image generated based on this response. This figure showcases four success cases and one failure case of our Diffusion models in generating high-quality images that align with the initial response.
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Query: Are there total of two dogs the picture? Describe relevant details about this.
Initial Response
Yes, there are total of two dogs in the picture. One of the dogs is lying down and looking the computer, resting its head on the keyboard. The other dog is visible just behind or to the side of the main dog.
Correct Generation
Query: there an elephant in this image? Describe relevant details about this.
Initial Response
Yes, there is an elephant in the image. It is baby elephant with gray and muddy coat, standing mud with ears spread out.
Correct Generation
Query: Are there two bowls in this image? Describe relevant details about this.
Initial Response
Yes, there are two bowls image, and
both are filled with fruit salad containing fruits such strawberries, blueberries, mango, and walnuts. The bowls sitting on table or counter top.
Correct Generation
Query: Are there three remotes in this image? Describe relevant details about this.
Initial Response
Yes, are remotes in this image. them are white, similar a Nintendo remote, while one has more traditionalllooking design. They are placed on top of bed, which has white handle comforter on top.
Correct Generation
Query: Are there three neckties in the image? Describe relevant details about this.
Initial Response
Yes, there is image of bald man wearing three different neckties, indicating a playful and unique sense of style.
X Incorrect Generation Figure D4: Qualitative evaluation of images generated by the generative model on the count subset of the MME benchmark. Specifically, the left displays the original image input, the middle presents the initial response generated by the LVLMs, and the right shows the image generated based on this response. This figure showcases four success cases and one failure case of our Diffusion models in generating high-quality images that align with the initial response.
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Published as a conference paper at ICLR 2025
Query: Are the pedestrians on the left of the bus? Describe relevant details about this.
Initial Response
Yes, the pedestrians are on the left side of the
bus. One pedestrian is standing near the road, while other people are waiting line board the yellow city bus.
Correct Generation
Query: Is the TV the right of the lamp? Describe relevant details about this.
Initial Response
Yes, the on the right of the lamp. The room appears to old-style, and there couch accompanied by television. There are also several TV remotes on the table near the couch, indicating the presence multiple control options TV.
Correct Generation Query: the refrigerator on the left side of the picture? Describe relevant details about this.
Initial Response
Yes, refrigerator located on the left side of the picture. It has a picture placed on the refrigerator door.
Correct Generation
Query: Is the sea behind people in the image? Describe relevant details about this.
Initial Response
Yes, the behind people the image. Located sandy beach area, the
riding horses, and the distance, there ocean, providing picturesque and serene setting for the
Correct Generation
Query: Is the vase on the right of the bottle? Describe relevant details about this.
Initial Response
Yes, the vase on the right of the bottle. It sits table near the kitchen counter with tray holding the gourmet food.
X Incorrect Generation
Figure D5: Qualitative evaluation of images generated by the generative model the position subset of the MME benchmark. Specifically, the left displays the original image input, the middle presents the initial response generated by the LVLMs, and the right shows the image generated based on this response. This figure showcases four success cases and one failure case of our Diffusion models in generating high-quality images that align with the initial response.
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Published as a conference paper at ICLR 2025
Query: Is there blue and orange fire hydrant in the image? Describe relevant details about this.
Initial Response
The image features a blue and orange engine company fire hydrant the side of the street. large, metal fire hydrant near an intersection.
Correct Generation
Query: Is there a red hat in the image? Describe relevant details about this.
Initial Response
Yes, dog wearing red hat is notable aspect of this image. The dog wearing green top hat as well, which adds to the entertaining and whimsical nature of the scene.
Correct Generation
Query: Is there living room painted yellow image? Describe relevant details about this.
Initial Response
Yes, the image shows living room that painted yellow. It old yellow living room.
Correct Generation
Query: there brown and black dog in the image? Describe relevant details about this.
Initial Response
Yes, there brown and black dog given name Max image, which likely Dachshund. This dog is laying on its side next to person. possibly on desk laptop.
Correct Generation
Query: there a bicycle with white handlebars the image? Describe relevant details about this.
Initial Response
Yes, there is an old red bicycle sitting locked up against pole, featuring pink handlebars. It is parked underneath a parking meter, indicating that the owner has tied it the bike rail to ensure its security while they are away.
X Incorrect Generation Figure D6: Qualitative evaluation of images generated by the generative model the color subset of the MME benchmark. Specifically. the left displays the original image input, the middle presents the initial response generated by the LVLMs, and the right shows the image generated based on this response. This figure showcases four success cases and one failure case of our Diffusion models in generating high-quality images that align with the initial response.
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Reviews:
[{"review_id": "o9ncPRybIQ", "reviewer": "Reviewer_XJFu", "summary": "This paper proposes to mitigate hallucinations in LVLMs by introducing valuable feedback from text-to-image generative models. Then a training-free decoding algorithm is introduced to integrate information from the original images and feedback from text-to-image generative models. Experiments on five benchmarks and two LVLMs demonstrate the effectiveness of the proposed method.", "questions": "Please see Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe idea of leveraging text-to-image generative models for LVLM hallucination mitigation is novel and interesting.\n2.\tThe paper is well-written and easy to follow.\n3.\tExtensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed method.", "weaknesses": "1.\tThe effectiveness of the proposed method is heavily influenced by the quality and realism of the generated images. So the authors should perform more experiments and analysis on the quality of the generated images (both quantitative and qualitative, especially for long captions and real images).\n2.\tSince the model relies on an addition diffusion model for inference, additional inference overhead should be discussed.\n3.\tExperiments are limited to two LVLMs, experiments on more recent LVLMs (e.g., QWenVL, MiniCPMV, etc.) should be conducted.\n4.\tIt’s better to perform experiments on multiple object hallucination [1].\n5.\tThe approach involves various hyperparameters that require careful tuning to achieve optimal performance, which could make practical implementation challenging.\n\n[1] Chen X, Ma Z, Zhang X, et al. Multi-object hallucination in vision-language models[J]. arXiv preprint arXiv:2407.06192, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MkT61k7aXH", "reviewer": "Reviewer_aaN5", "summary": "Recent large vision-language models (LVLMs) have demonstrated impressive performance in multi-modal tasks, but they are prone to generating hallucinated text responses that do not align with the given visual input. This work explores the potential of leveraging powerful text-to-image generative models to assist in mitigating these hallucinations. The authors discover that generative models can offer valuable self-feedback for correcting hallucinations at both the response and token levels. Building on this insight, they introduce a novel training-free decoding algorithm called \"self-correcting Decoding with Generative Feedback (DeGF)\" that incorporates feedback from text-to-image generative models to recursively enhance the accuracy of LVLM responses. Extensive experiments validate the effectiveness of DeGF in reducing diverse types of hallucinations, consistently outperforming state-of-the-art methods across multiple benchmarks.", "questions": "1. How about using the middle step of Stable Diffusion to provide generative feedback and reduce the computation costs?\n2. How about integrating the generative feedback in the instruction tuning phase? It will avoid inference costs.\n3. Why not conduct GPT4/GPT4-V analysis like VCD [r1]?\n[r1] Mitigating object hallucinations in large vision-language models through visual contrastive decoding. CVPR2024.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Providing the generative feedback to mitigate hallucinations is straightforward and reasonable. Token-level refinement based on Jensen-Shannon divergence correctly utilize the generative feedback.\n2. The proposed Self-Correcting Decoding with Generative Feedback (DeGF) also achieves pleasant results on POPE, CHAIR, MME, etc.\n3. This paper is well-written and has clear figures. The experiments are extensive to some extent, and clearly organized.", "weaknesses": "1. The computation costs are unafforable for the LLM decoding strategy. Utilizing generative model like Stable Diffusion to provide generative feedback is unrealistic for practical deployment. Moreover, self-correcting decoding also consumes twice inference costs similar to contrastive decoding.\n2. This approach utilizes extra pretrained network (i.e., Stable Diffusion). Baselines should contain methods that also employ extra analysis network like woodpecker [r1, r2], etc. Otherwise, it is unfair for other decoding methods.\n[r] Woodpecker:Hallucinationcorrectionformultimodal largelanguage models. arXiv:2310.16045,2023.\n[r2] HALC:Object hallucinationreductionviaadaptivefocal-contrastdecoding. In International Conferenceon MachineLearning, 2024.\n3. No inference costs are listed and compared, which is important for LLM decoding strategies.\n4. Experiments only comducted two LVLM backbones. More results like on QwenVL are better performed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XTpuWI8isT", "reviewer": "Reviewer_QadT", "summary": "This paper proposes self-correcting decoding with generative feedback (DeGF), a novel training-free approach that leverages feedback from diffusion models and corrects decoding procedure, either by contrastive or complementary manner. This paper has well grounded their approach on thorough analysis and ablations, surpassing previous approaches on five hallucination benchmarks.", "questions": "1. May I ask how many samples authors use for plotting Figure 3?\n\n2. For Table 2, may I ask if authors have tested long sequence, by setting max new tokens to larger numbers, such as 128 and 256?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear writing. Presentation and writing are clear and easy to follow.\n\nWell-motivated. A clear negative correlation between hallucination rates and CLIP similarities can be observed (Figure 3), which gives a strong empirical foundation of proposed decoding approach.\n\nSufficient experiments. Authors do sufficient ablations, discussions to support their claims. Performance of proposed DeGF is quite good.", "weaknesses": "A concern originated from numerical hallucinations. Figure 2 presents an overview of proposed approach DeGF, with addressing numeric hallucinations as an example. A key premise for this method is that diffusion models can accurately perceive numbers. However, it seems a common observation that diffusion models fail to accurately interpret numbers [A]. Have authors considered the case when diffusion models fail to generate numerically accurate images? It is good to include analysis on tolerance of proposed DeGF to diffusion model failures.\n\nIncreased test-time computation cost. Diffusion models take dozens of inference steps to generate images, which is computationally less efficient than other decoding approaches.\n\nAuthors are suggested to include test-time computation comparison to complete this work.\n\n[A] Mass-Producing Failures of Multimodal Systems with Language Models. NeurIPS 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "703VLU4UJH", "reviewer": "Reviewer_prZa", "summary": "The study proposes a new decoding method called Decoding with Generative Feedback (DeGF), which utilizes self-correcting feedback from text-to-image generative models to improve accuracy. By comparing original visual content with generated visuals based on responses, DeGF refines outputs through contrastive predictions. Extensive evaluations show that DeGF enhances performance across benchmarks, effectively reducing various types of hallucinations. However, DeGF faces limitations due to its time-consuming image generation process, limited benchmark evaluations, and inconsistencies in experimental results—particularly on the POPE benchmark compared to LLaVA-1.5—raising concerns about its suitability for real-time use and overall reliability.", "questions": "-  Could the authors clarify the specific settings followed in the experiments on POPE benchmark?  How do these settings differ from those used in LLaVA?\n- Could authors follow LLaVA’s setting and conduct more extensive evaluations on comprehensive benchmarks like MMbench and MMVet, given their importance for assessing the model's overall performance?\n- Is this decoding method useful in more advanced VLLMs, such as Qwen-VL, Intern-VL, etc.?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- DeGF Leverages generative models to provide self-feedback by comparing the original visual input with a newly generated image, which helps in correcting hallucinations at both response and token levels.\n\n- DeGF  demonstrates the capability of DeGF to handle diverse hallucination types, including object existence, visual appearance, and counting, showing generalizability beyond just language biases.\n\n- DeGF  offers an efficient, cost-effective alternative to existing methods as it avoids the need for additional data or training, focusing instead on refining the decoding process.\n\n- The study finds that text-to-image generative models effectively identify hallucinations. Higher discrepancies in token predictions, measured by JS divergence, indicate hallucinations, showing that generative feedback can highlight inaccuracies both broadly and specifically.", "weaknesses": "- The process of generating images adds time, potentially slowing down LVLM response generation and making it less suitable for real-time applications.\n- The authors did not perform more extensive evaluations on comprehensive benchmark such as MMbench, which is crucial for assessing the method's overall performance.\n- I am confused by the experimental results about POPE, as they do not seem to fully align with the result from LLAVA 1.5.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)