Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Authors: Sreyan Ghosh
Chandra Kiran Reddy Evuru
Sonal Kumar
Utkarsh Tyagi
Oriol Nieto
Zeyu Jin
Dinesh Manocha
Venue: ICLR 2025 Poster (2025)
Keywords: lvlm
hallucinations
reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose a novel method to reduce hallucinations and improve LVLM performance on cognitive prompts requiring reasoning and knowledge retrieval.

Abstract: Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, the exact causes behind them remain underexplored. In this paper, we first investigate the root causes of hallucinations in LVLMs. Our findings reveal that existing mitigation techniques primarily reduce hallucinations for visual recognition prompts—those that require simple descriptions of visual elements—but fail for cognitive prompts that demand deliberate reasoning. We identify the core issue as a lack of true visual perception in LVLMs: although they can accurately recognize visual elements, they struggle to fully interpret these elements in the context of the input prompt and effectively link this recognition to their internal knowledge, which is critical for reasoning. To address this gap, we introduce Visual Description Grounded Decoding (VDGD), a simple, robust, and training-free method designed to enhance visual perception and improve reasoning capabilities in LVLMs. VDGD works by first generating a detailed description of the image and appending it as a prefix to the instruction. During response generation, tokens are sampled based on their KL divergence to the description, favoring candidates with lower divergence. Experimental results on multiple visual reasoning benchmarks and LVLMs demonstrate that VDGD consistently outperforms existing baselines  2% - 33%. Finally, we introduce VaLLu, a benchmark designed for comprehensive evaluation of the cognitive capabilities of LVLMs.

Published as a conference paper at ICLR 2025
VISUAL DESCRIPTION GROUNDING REDUCES HALLU- CINATIONS AND BOOSTS REASONING IN LVLMS
Sreyan Ghosh", ", Chandra Kiran Reddy Evuru', Sonal Kumar"', Utkarsh Tyagi',
Oriol Nieto', Zeyu Jin', Dinesh Manocha' 'University of Maryland. College Park, USA, 'Adobe, USA (sreyang, sonalkum) @ umd.eddu "equal technical contribution.
Project:
github..iitth...... 1o/VbDD/
ABSTRACT
Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations, While halluci- nations are well-studied, the exact causes behind them remain underexplored. In this paper, we first investigate the root causes of ballucinations in LVLMs. Our findings reveal that existing mitigation techniques primarily reduce hallucinations for visual recognition prompts- those that require simple descriptions of visual elements-but fail for cognitive prompts that demand deliberate reasoning. We identify the core issue as a lack of true visual perception in LVLMs: although they can accurately recognize visual elements, they struggle to fully interpret these elements in the context of the imput prompt and effectively link this recognition to their internal knowledge, which is critical for reasoning. To address this gap, we introduce Visual Description Grounded Decoding (VDGD), simple, robust, and taining--ree method designed to enhance visual perception and improve reasoning capabilities in LVLMs. VDGD works by first generating a detailed description of the image and appending it as prefix to the instruction. During response generation, tokens are sampled based on their KL divergence to the description, favoring candidates with lower divergence. Experimental results on multiple visual reasoning benchmarks and LVLMs demonstrate that VDGD consistently outper- forms existing baselines 2% 33%. Finally, we introduce VaLLu, benchmark designed for comprehensive evaluation of the cognitive capabilities of LVLMS.
INTRODUCTION
Large Language Models (LLMs) pre-trained on web-scale text data with the next token prediction objective implicitly compress world knowledge in their parameters (Zhao et al.] 2023). These models learn general-purpose representations, which can then be aligned with the desired response characteristics (Zhang et al. 2024b). This step generally involves fine-tuning on instruction-response pairs, also known as instruction tuning (TT) (Wei al. 2022). and is followed by an optional step of reinforcement learning from human, feedback (RLHF) (Bai et al. (2022). Such aligned LLMs can be engaged through text-based inputs. or prompts, directing them to perform various tasks, including those based on information retrieval and reasoning.
Recent advancements in the research community have demonstrated success in multi-mode alignment, whereby fine-tuning LLMs with multi-modal instruction-respont pairs enables them to execute tasks that conform to the text prompt as well as additional multi-modal inputs (Yin et al. 2023a). Since their introduction, the research community has extensively evaluated the capabilities of Large Vision- Languue Models (LVLMs) across areas such as visual recognition (Wang et al.) 2023a), perception (Fu et al.) 2024), and reasoning (Yue et al.) 2024). Researchers have made significant efforts to enhance these capabilities by refining model architectures and training techniques (Zhang al. 2024a) by implementing strategies mitigate hallucinations/ (Huang al. 2023a).
Among these approaches, training-free hallucination mitigation techniques stand out as a cost- effective and accessible method to enhance LVLM performance without the need for extensive
'Hallucination refers the mismatch between factual content and the model's generated responses. Mitiga- tion aims to reduce these discrepancies for more accurate outputs and improve task performance.

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computational resources. However, current hallucination mitigation techniques primarily focus on reducing hallucinations and improving the performance of visual recognition tasks (e.g., describing a scene, OCR, etc). Our extensive experiments show that these techniques fall short when applied to cognitive prompts requiring reasoning or knowledge extraction, indicating a significant gap in their ability to address hallucinations in more complex, geasoning-intensivy scenarios.
To this end, we propose Visual Description Grounded Decoding (VDGD), a simple and novel training-free technique designed to improve LVLM reasoning by reducing hallucinations for cognitive prompts. Much like humans, who often write down their observations of an image in their own words and refer back to them while tackling complex reasoning tasks, we hypothesize that grounding the LVLM's response generation in an explicit visual description can significantly aid its reasoning capabilities. Specifically, we first generate a description of the input image. Next, at every decoding step during auto-regressive response generation, we calculate the Kullback-Leibler divergence (KID) between the top-k tokens in the current logit and all the logits of the image deseription. Finally, to predict the next word, we sample from the plausible candidates according to their KLD to the description, where lower KLD is given higher preference. By continuously referencing its descriptive summary of the visual content, the LVLM maintains alignment between the visual input and its generated responses, thereby reducing hallucinations. To summarize, our contributions are as follows:
1. Through extensive experiments, we categorize hallucinations in LVLMs and show that existing mitigation techniques work well for visual recognition prompts but fail for reasoning- intensive cognitive prompts.
2. Inspired by this, we identify critical visual perception gap in LVLMs: while they can recognize visual elements and reason, they struggle to contextualize visual information with the prompt, leading to hallucinations in cognitive reasoning tasks.
3. We introduce VDGD, novel and training-free method that enhances LVLM reasoning by grounding response generation in visual descriptions. We evaluate VDGD on 8 benchmarks with prompts that require deliberate reasoning and how VDGD significantly outperforms existing techniques, improving performance by 2% 33% by reducing hallucinations.
4. Finally, we introduce VaLLu, a meticulously curated benchmark designed to evaluate the cognitive capabilities of LVLMs.
2 RELATED WORK
Large Dision-Langual Models In recent years, Large Vision-Language Models (LVLMs) have seen rapid advancements with the release of numerous new models. These developments have been driven by innovations in model architectures (Liu et al.] [023b] Ye et al.) 2023] Wang et al.) 2023b). training methods (Liu et al., (023b), alignment techniques (Chen et al., [024b), ) and data augmentation techniques (Liu et al.) 2023b Wang et al.) 2024b). To assess the capabilities of these models, the research community has introduced a variety of benchmarks specifically designed to evaluate different aspects, including visual recognition and understanding in diverse scenarios (Liu ct al. 2023a Kim et al., 2022), reasoming (Lu et al. 2023 Yue et al. 2024 Bai et al. 2024 Liu et al. 2024b), mnowledge-based information retrieval (Hu et al. 2023) and other specialized tasks.
Reducing Hallucinations to Improve Response Quality: Hallucination mitigation broadly aims to enhance the accuracy of model responses. While numerous studies have proposed methods to reduce hallucinations and improve response accuracy (Sicong Leng et al.) 2023 Huang et al., [223b] Yin
et al. 2023b Zhou et al. 2023b) and evaluated their effectiveness (Wang al. 2023a Guan et al. 2023), most Tocus on mitigating object hallucinations in visual recognition tasks. While these efforts are valuable, they fall short of assessing the true reasoning capabilities of LVLMs. Moreover, despite the growing focus on evaluation and mitigation, the underlying causes of hallucinations remain largely unexplored. Recent methods like Compositional Chain-of-Thougee Prompting (CCoT) et al.. 2024). Visual Table(Zhong et al.] (024), and Visual Evidence Prompting (Li et al.) 2024) introduce structured intermediate representations (e.g., scene graphs or hierarchical descriptions) to improve reasoning. While these methods show strong performance on real-world scenes, they rely heavily on explicit object-centric representations and often struggle with abstract or non non-real-wood world inputs (e.g.. charts mathematical graphs). Additionally, methods like Visual Table not training- raining-free and require substantial computational resources. In contrast, VDGD is lightweight, training-fiee free, and generalizes effectively to diverse inputs, including abstract datasets like MMMU and Math Vista.
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3 HOW CLOSE ARE WE TO RELIABLE LVLM RESPONSES?
LLaVA-v1.5 1.5, LLaVA-v1.6 mPLUG-Ow/2, InternLM-XX, and CogVL.M- all built on a 7B parameter language model. The models are tested across seven benchmarks: AMBER (visual recognition), SynthDoG (OCR), MMMU (expert-level reasoning). MathVista and MATH-Vision (mathematical reasoning), MMC (chart understanding), and MME and HallusionBench. To address potential limitations in existing evaluation techniques.
Experimental Setup for Analysis. We evaluate six LVL.M-LLLaa..
metries, which may not fully capture reasoning skills and often rely on we employ combination of expert human evaluation and LLM-as-a-Judge (using GPT-4-turbo-22 04-09). This paradigm has been extensively followed by prior studies in LLM evaluation (Zhou et
2023a: Ghosh et al. 2024) Zheng et al.] 2023) and hallucination evaluation (Sicong Leng et al. 2023 Yu ct al. 2023 Liu et al. 2023c). We devise an evaluation prompt that penalizes hallucinations and assigns scores from to 5 based factual correctness (sce Appendix G This approach also helps us standardize scoring across benchmarks and has higher correlation (=0.97%% to expert human evaluations (see Section D Quantitative results for all graphs in the paper are in Appendix L
Preliminaries. As LVLMs advance and become more accessible, their application extends beyond merely generating image descriptions. Thus, to better understand visual perception and reasoning, we break down information processing by an LVLM into its constituent steps:
Visual Recognition
Knowledge Extraction (optional) Reasoning (optional)
Perception
Figure demonstrates that different prompts applied to a single image can as- sess various LVLM skills. Visual Recog- nition (VR) focuses on identifying visual elements and their relationships within the image, such as describing objects or specific details. Visual Perception (VP) extends beyond VR by interpreting and
Revenue in million U.S. dollars
Visuel Recogaitiio (VR) Descille tiis image
1,00
Porcsption (VP) revema: incras 2015?
1,909
changee
VP- Knowledge Extraction Reasoning What fucton might fooe commberad incrcasc casino garnin masket reverue the United Kingdon betwee 2006 and 2015
contextualizing these elements within the broader scene (Fu et al.] (024), essen- tial for tasks requiring more than ba- sic recognition. Prompts that demand knowledge-specifit insights (also known as information-seekle seeking prompts) engage knowledge extraction (KE) learned from pre-training Finally, prompts requiring reasoning involve combining visual data, textual prompts, and extracted knowledge to generate response. While recog- nition depends on the vision encoder, knowledge extraction, and reasoning rely on the foundational language model. VP, implicitly learned during alignment (Ghosh et al.) 2024: Zhou et al. 2023a)), bridges VR and higher cognitive functions, facilitating comprehensive understanding of the image in context to the prompt (Barrow et al. 1978 Torralba & Oliva 2002 Hartley Zisserman 2003).
VI- Reasmiing Are there any initution to
what cas be leareed from this chart?
Figure 1: Depending on the instruction, LVLM might assessed one or more different capabilities.
3.1 Do HALLUCINATION MITIGATION TECHNIQUES IMPROVE COGNITIVE ABILITIES?
Fig. (lett) compares the performance LVLMS across 5 benchmarks. As clearly see, while newer mod- els that were trained using improved architectures and alignment training data boosted performance on the AM- BER benchmark (for recognition), performance other reasoning and information-seeking seeking benchmarks re- mained stagnant.
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LLAAA
Figure 2: (Left) Performance comparison of different LVLMs on various benchmarks. (Right) Performance comparison of different hallucination mitigation techniques applied to L.aVA-1.5. explicit hallucination mitigation techniques, with strategies ranging from improving model
Fig. 2 (right) compares the perfor- mance of LLaVA-1.5 across the same 5 benchmarks when employed with
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Types of Visual Understanding Hallucination
Vision
Hallucination
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Style Hallucination
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Languige Hallucination
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Figure 4: Types of Visual Recognition Hallucinations. We define Algo. to divide VR hallucinations into 4 different categories automatically: Language. Vision, Style, and IT (explained further in Sec. S.3. While language and vision hallucinations have been explored earlier, and methods alleviate them have been proposed, we show for the first time that Style and IT hallucinations exist and existing methods fail to alleviate them.
architectures to explicit response correction strategies, namely, Visual Contrastive Decoding (VCD) (Sicong Leng et al. (023), OPERA (Huang et al.) 2023b), Woodpecker (Yin al. 2023b) and LURE (Zhou al.) 2023b). As clearly see, while all these methods boost performance AMBER, performance on other reasoning and information seeking benchmarks remains stagnant.
3.2 Do MITIGATION TECHNIQUES GENERALIZE BEYOND REAL- WORLD SCENES?
As LVLMs are now being employed on a wide range of tasks, consider- able research efforts are being made to evaluate the efficacy of L.V.MMs to reason on images beyond real-world scenes, i.e., charts (Liu et al. or math problems (Lu et al. 2024).
PMU
2023a)
However, how often do LVLMs hal- lucinate while describing images be- yond real-world scenes? Fig (left) compares the performance of vari- ous LVLMs across benchmarks. MMMU and Math Vista include im- ages with mathematical figures and MMC includes charts from diverse sources. On the other hand, Synth- DoG prompts the model for OCR and AMBER for image descriptions for real-world scenes. As we clearly see, compared to AMBER, models hallucinate more often when describing visuals beyond real-world scenes. Fig 3 (right) further shows that hallucination mitigation techniques improve performance on AMBER but rarely other benchmarks.
NATHIA
NMC LANE
RATH-O0
(VANBA))
Figure 3: (Left) Performance comparison of different LVLMs on various benchmarks when prompted to only describe the image. (Right) Performance comparison of different hallucination mitiga- tion techniques applied to L...
3.3 ARE ALL HALLUCINATIONS EQUAL?
Overview. In our detailed analysis of model responses on the AMBER dataset, we identified in- triguing patterns in which hallucinations occur. Although the community has primarily focused on the broader topic of object hallucinations (Liu et al. 2024b), our findings reveal that visual recog- nition ballucinations can be classified into several subcategories based on their specific occurrence patterns. Specifically, we perform the token-distribution analysis proposed by (Lin et al. 2023).
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For given Instruction-respoonee -imgg triplet, the instruction é = f81, i2. ...) and the image are first imput to the aligned (or fine-tuned) model to obtain its response r = (ri, f2,' "* via greedy decoding. Next, for each position in the response, 'context' at this position is defined as to be 2t = (ri..... -r...--11) Tr-1). This "context" is then input to the base model (model from which the aligned model was instruction- tuned) to obtain its probability distribution for predicting the next token at position I, Prase. We then define Base Rank as follows: Rank in P base of the token at with the maximum Palign value. With the base rank denoted as 17, the unshifted, marginal and shifted tokens are defined as when
Algorithm 1 Categorizing Visual Hallucinations
Given: Medel Response
Pry. Aligned LVLM Malgadd:
Muse
Oseann lop similar instances Sgt to from Pir using CLIP Extract visual clements V from R.
Extract ballucinated Relation, Action hnlllccinaiinss precedure FOKEINANiLYSti RR, Malggadd Musse) for each word in do 1 then Calculare Rese- Rese-Rank if BR > then af in 8 then p Hallsscination
P from. using LLM jadge divided into Object,
Consider only
n
p. Defined Section B.
D marginal or shifted token
if pm Relation & p Deject then Style Hallucination
pisa Vision Hallucination
cnd if
end
unchifted token
(n) 0), (0 < 7 S 2) and (1) 2) respectively. Next, we use our judge (GPT-4) to extract the exact hallucinated phrases based on types: ob- ject, relation, and action hallucinations. Finally, based on the Base Rank formulation and the hal- lucinated phrases, we propose Algorithm to categorize identified hallucinations into 4 different categories based on their cause. The algorithm is further verbally described in Appendix [
p Language Hallucination end end for and prscedure
and
(1) Language Hallucinations: These are hallucinations that are caused when LVLMs over- rely on the language priors (or prior tokens in the response) rather than the input im- age. We attribute all hallucinated tokens that are unshifted as language hallucinations.
(2) Vision Hallucinations:
Unlike language hal-
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lucinations, the tokens for vision hallucinations are generated by attending to the input image and are shifted or marginal tokens. These are hallucinations caused when the model fails to accurately recognize visual elements in an input image inaccurate reasoning or knowledge traction, among other factors.
1250
250
600
(3) 1.3 Hallucinations. These are hallucinations caused by style imitation. Open-source VLLMS often trained on instruction-tuninn data syn- thesized from proprictary closed-source mod- els. Training on such datasets makes the VLLM learn to mimic the characteristics of responses in the IT dataset (Ghoss et al., 22024, Gudibande al. 2024) (e.g., lengthy answers, summary line at the end of the response, etc.). How- ever, owing the lack of sufficient knowledge to mimic its closed-source counterparts, open- source VLLMs might hallucinate facts while responding (Ghosh et al., 2024)..
290
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LLWWW-15
VCD
OPTRA
LURE
1230
292
590
250
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OPERA
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LURE
(4) Instruction Tuning (TT) Hallucinations. These are hallucinations caused by biases learned during the instruction tuning stage. LLMs are known to mimic the responses as- sociated with the unfamiliar examples in the model's instruction-tuniin data (i.e., those unfamiliar to the pre-trained base model) (Ghosh et al. 2024). Formally, this can be defined as distribution shift. As visual instruction tuning is an extreme case of such a distribution shift, hallucinations are a major cause of hallucinations in L.MMs.
Figure 5: (Left) Frequency comparison of hallucination categories. (Right) Comparison for LLaVA-v1.5 with hallucination miligation techniques. The top graph com- pares AMBER, and the bottom graph compares MMMU.
Performance Comparison. Fig. 3 (top:left) compares the frequency of different categories of hallucinations across various LVLMs for AMBER. Fig. (top: right) displays the frequency of differ- ent hallucination categories for LLa VA-v1.5 alongside various hallucination mitigation techniques.
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Fig. 5 (bottom) offers similar comparison for MMMU. Our key findings include: (1) Model-based improvements and mitigation strategies reduce language and vision hallucinations, but progress in style and IT hallucinations remains limited. (2) Specific mitigation techniques are more effective for certain types of hallucinations. For e.g., decoding- soadingg-bsed methods like VCD and OPERA excel at reducing language hallucinations, whereas grounding approaches like Woodpecker for vision hallucinations. Decoding-basee techniques benefit from low-confidence ballucinated tokens and uncertainty encoded in logits discussed next. More discussion is in Appendix K.I (3) Existing
effective
methods fail to reduce ballucinations on reasoning benchmarks like MMMU. We attribute this to the algorithmic biases of these methods that are crafted to just reduce object hallucinations.
Key Takeaway: Current hallucination mitigation techniques mainly improve LVLMs' visual recognition skills but are less effective in improving other cognitive abilities, such as reasoning. Additionally, these improvements are largely limited to real-world scenes and specific types of hallucinations, leaving areas like ree-real-oolld scenes largely unexplored.
4 THE VISUAL PERCEPTION GAP: LVLMS CAN SEE BUT NOT PERCEIVE
It is evident that current hallucination mitigation techniques primarily enhance visual recognition but fail to improve other cognitive abilities, such as reasoning or knowledge extraction. In this section, investigate the underlying cause of this limitation. Our analysis reveals that LVLMs often rely on language priors rather than attending to the ipput image when generating responses to reasoning prompts. This leads to identify critical issue: the visual perception gap. While LVLMs can accurately recognize visual elements and possess the necessary knowledge and reasoning skills to respond factually, they struggle to perceive and interpret these elements in relation to the input prompt. This gap in perception causes hallucinations, resulting in incorrect responses.
4.1 VISUAL. BLIND SPOTS: LVLMS IGNORE THE INPUT IMAGE FOR REASONING
With findings from previous sections, now as- sess the influence of the image on the LVLM re- sponse. Precisely, we compare the Base Rank (de- fined in Section 3.3 of tokens across datasets. A lower value would signify that the model solely re- sponds using language priors, while higher value would signify that the model pays attention to the image, i.e., it responds with tokens different from what the model would have responded with only lan- guage priors. Fig. 6 plots the Base Rank of tokens against the token position in response (average across the dataset). As see, the Base Rank for AMBER, which prompts for an image description, is higher than Math- Vision, which requires it to reason or ex- tract knowledge based on the image. This hints to- wards an alignment issue: different from AMBER, for the image before responding, under-relying on visual cues and over-relying on language priors for generating responses, In Appendix K.T we explain why decoding- based mitigation techniques discussed in Section 3.3 do not work for reaosning in cognitive prompts.
Datnoet AMMia Hemmiiion
Base Bank
Tokon Pozzion
Figure 6: Base Rank Comparison between AM- BER and HLATH-Vision datasets a function of token position in responses (for VLM).
Math- Vision LVLMs are unable perceive
4.2 DISENTANGLING VISUAL RECOGNITION FROM OTHER COGNITIVE SKILLS
A key objective of alignment fine-tuning is to equip LVLMS with implicit visual perception skills. As outlined in Section 3. LVLMs should interpret images in context and respond ac- curately to information-ssekking
reasoning prompts. To determine if hallucinations arise from the base LLM's limitations or alignment gaps, we aim to isolate visual recognition from other cognitive skills and evaluate them separately. We use GPT-4 to rephrase prompts from visual cnfformation-seeking and reasoning benchmarks, modifying them so they can be answered without the image (examples in Appendix K.7 and prompt used in Appendix G

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LVLMs are first evaluated on these text-only prompts and then on gener- ating independent image descriptions (evaluation prompt in Appendix G). Figure 7 (left) compares the perfor- mance of LVLMs on the text-only prompts (marked with -f) against their original image-text versions, while Figure (right) shows VR scores. Our results indicate that LVLMs excel in visual recognition and perform bet- ter on factual responses when using text-only prompts than with combined image-text inputs. This suggests that LVLMs have the necessary knowledge and reasoning skills but struggle to integrate these with visual perception in prompts requiring both. We identify this as perception gap: LVLMs can recognize visual elements but fail to fully perceive them in a way that connects recognition to internal knowledge, which is essential for reasoning information extraction. This challenge likely arises due to several factors: (i) training datasets that are largely composed of dengeeddscriition pairs, (ii) aligmment datasets that contain strong visual cues within the natural- -aatuulll prompts.
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Figure 7: (Left) Performance comparison of different LVLMs when prompted w/ original prompt vs rephrased prompts w/o image (-r). (Right) Performance comparison of different LVLMSs for their ability to generate a faithful image description.
4.3 UNCERTAINTY IN THE LOGIT SPACE
To investigate the origins of hallucinations, we analyze the logit space of hallucinated tokens. Specifically, after applying the softmax opera- tion to the logits, we rank them by their prob- ability scores. We then use the elbow method (detailed in Appendix K.6) to truncate the dis- tribution, obtaining the top-k tokens along with their corresponding probabilities. This trunca- tion approach, based on the elbow method, ef- fectively identifies the tokens most likely to be selected during multinomial sampling. Table
Models
Dataset
Variance
Range
LLaVA-v1
AMBER
1.1/4,2
0.0/0.1
0.0
LLaVA-v1.5
AMBER
1.0/3,9
0.0/0.1
0.0/0.4
CogVLM
AMBER
1.0/3.4
0.0/0.1
0.0
LLaVA-v1
MMMU
1.2/3.7
0.070.1
0.0
LL.VA-v1.5
MMMU
1.2/3.5
0.0/0.2
0.0/0.4
CogVLM
MMMU
1.1/3.2
0.0/0.2
0.0
Table 1: Post-truncation probability statistics using the elbow method. All values are in the format: non- /hallueinated averaged across all tokens in the dataset. shows several statistics of the top-k probabilities for hallucinated tokens (we only consider the first token of each word). As evident from the value of k, standard deviation, and the average, the top-k probability space for hallucinated tokens is dominated by a set of low and equally confident tokens. Thus, each of these tokens has almost similar chance of being sampled with multi-nomial sampling.
Key Takeaway: LVLMs generally demonstrate accurate recognition and cognition skills to respond and reason accurately. However, they struggle to effectively link they recognize to their internal knowledge during the reasoning process, which results in inaccurate responses. This issue underlines what we identify as the visual perception gap.
VISUAL. DESCRIPTION GROUNDING DECODING (VDGD)
Main Motivation. To bridge the visual per- ception gap and reduce hallucinations for cog- nitive prompts, we propose Visual Description Grounding Decoding (VDGD), simple, effec- tive, and training-free hallucination mitigation technique. During decoding, VDGD first ap- pends the image description (generated by the
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LVLM itself) as a prefix to the prompt's text instruction, then grounds token generation by se- lecting the token that deviates the least from the description. Token deviation is measured using novel formulation based on Kullback-Leeiblrr
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Figure 8: Illustration of our proposed VDGD method.
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divergence of the token with the description generated by the LVLM itself. Much like humans, who often write down their key observations in an image and refer to them while solving complex reason- ing tasks, hypothesize that grounding the LVLM's responses in detailed visual description can significantly enhance its reasoning process. Additionally, re-scoring the equally and low-confidence tokens (discussed in Section I based on deviation from the description can increase the confidence of the accurate token from several low-confidence tokens (Xu et al. 2023).
5.1 METHODOLOGY
Fig- 8 iillsstrates the VDGD methodology. For an input prompt, we first employ the L.L.M to generate description of the image accompanying the prompt (sce Appendix G for the prompt). Next, we concatenate the generated description as a prefix to the original prompt.
Formally put, let the VLM, denoted as PVLM- receive an input prompt of length symbolized as Xpee .I....z. with each 2; representing a token from the vocabulary V. The decoder's 's objective is to produce responses of length m, represented as Xes En+1, ... -n-m- During the decoding phase, the model iteratively decodes one token at a time, conditioning on the tokens that have been generated far:
n+n
PVLM (eesp
X
PVLM (ee)) i=++1
II
Here, PVLM (zt (<))) denotes the probability distribution for the next token, 2;, given the previous tokens. Inspired by our findings in Section 4.3 we first apply a plausibility constraint truncate the vocabulary space of the next token 2; (Li et al. 2023). Essentially, this step cuts off tokens from the vocabulary below the elbow and keeps only the top-k plausible tokens. This is done as follows:
PPLLM (xra xes), if PVLM (I; mees)= 2 a max PVLM (w xies) (-00,
PVLM (x:
| xei)
Otherwise
where a is hhppr--ppareeee parameter between [0.1]. Now let Vi be the set of top-K most plausible tokens from the vocabulary V after the truncation of z4. Next, for each token wx E Vi in the top-K plausible tokens, we calculate the deviation of the token with the n tokens the prompt as follows:
KL
min KL (one-hot (&) P|PLL (- (<z))) SSSa
(3)
where KL the Kullback- Leibler divergence and one-hot(w;) R is the one-hot representation
of token ua where each value is 0 except the position corresponding to the token-id of w, which is 1. Next, we replace the value of wi in the logit with -KT les and apply softmax on the logits to obtain a probability distribution. Thus, tokens with larger KLD (or large deviation) to the input prompt are less likely to be sampled given the softmax operation upon KL divergence. With this distribution, we can now apply any sampling technique sample the next token.
5.2 EXPERIMENTAL SETUP
Datasets and Evaluation Metrics. For evaluation, we employ variety of standard benchmarks focused on reasoning and information-sseking tasks. These include LLLVA--Bechh, MM-Vet (Yul al. 2023). MMBench (Liu et al.) 2023d). MME (Fu et al. 2023). Math Vista (test-mini subset), Math Vision, and MMMU (validation set). For each benchmark, we employ our proposed GPT evaluation technique described in Section S Beyond being more robust the diverse formats of model generation, we also find GPT scores to have a higher correlation to expert- humert-human evaluation (==0.77: Sectien D Dhas has more details with original benchmark evaluation metrics that has a correlation of 0=.92). We are inspired by wealth of prior studies in hallucination evaluation (Sicong Leng
et al. 2023 Yu et al. 2023) Liu et al. 2023c). All results are averaged across runs.
We compare VDGD against VCD, OPERA, Woodpecker, LRV, LURE, HALC (Chen et al.) 2024a) and PAI (Liu et al., 2024d) hallucination mitigation techniques. Additionally, we compare two vanilla decoding methods (without any additional mitigation techniques): Vanilla-greedy with vanilla-greedy decoding and Vanilla samiling with multinomial sampling-based decoding (top-p=0.5 and lemperature=0.7). We employ greedy decoding for all methods
Baselines.
find no difference in performance on sampling. For LVLMs, we employ LLaVA-v1, LLa LLaVA-v1.6, mPLUG- Ow12, Internl M-X, CLM, Qwen2-VI. (Wang et al. 2024h) and Cog VL.2 (Hong et al.! 2024).
8

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Published as a conference paper at ICLR 2025
Benchmark
LLaVoev
LLAVA-15
LLaNA-L6
mPLUG-OwD
InteraLM-X
CogVLM
CogVLM2
QwennZ-VL
Vanilla greedy
2.01
2,05
Vanilla sampling
1.27
1.40
2.10
1:34
1.92
1.44
1.51
2:23
2.14
2.64
OPERA
1.30
1,43
1.57
1.64
2.25
1.62
2.21
2.44
MMMM
Woodpoeker
1:32
1.44
1.3
1.61
2.12
1.65
2.14
2:56
LRV
1.29
1.49
161
1.58
2.06
1.59
2.18
2.46
LURE
1.31
1.47
1.50
164
2.27
1.06
2.27
2.71
PAI
1.42
1.39
1.44
1.56
7:23
1.65
2.29
HALC
T40
1,56
1,63
165
2.15
1667
2:24
2.69
VIGGD
1.49 (+189))
(12311))
(F238)))
2.39
2.47
2,91
Vamilla-gredyy
1.65
2.56
2,45
2.97
Vanilla sampling
1.9
2.50
2.95
1.67
1.68
2,07
2.59
2,81
OPERA
1.83
2.04
2.04
2,62
3.32
Math Vist
Woodpecker
1.72
2.10
2.19
2.13
2,43
2.43
2.59
3,13
LRV
178
1,68
2.07
139
2.61
2.20
2,62
3.30
LURR
1.50
2,03
2.21
2,07
2.37
2.45
2.49
3.13
PAI
1.68
1,35
1.97
2.88
2
2.20
2,50
3.39
HALCC
1.71
2,05
2.17
1.97
7.
2.34
2.77
3.38
VDGD (vurs)
1884 (+18%)
2.19
(4336)
2.44
(+225))
2.24 (+17%)
2,88 (+13%)
2,62
(+22%)
2.93
(+20%)
3.59
(+215))
Vanilia greedy
2,67
2.89
3.11
2.98
3.58
3.19
3.71
3.93
Vanilla sampling
2,69
2,92
3.12
3.59
3.22
3.68
3,95
VCD
2,50
3.09
3.00
3.22
3.81
4.20
3.93
MMBunch
Woodpeekkrr
2,83
296
3007
4.19
2.77
2.99
3.23
3.77
4.05
LURE
2.72
2.97
3,13
3,26
3.72
3.21
3.82
4.16
PAI
2.75
3,00
3.04
X.IG
3.94
3.35
3.97
4.05
HALC
2.73
2,95
3.02
3.18
3,81
3,27
4,04
4.08
VI&D (ours)
293 (-109))
2.12 (4R96)
3.26 (455)
3.32
4.08 (-149))
(4755))
4.15(CT725)
4.51 (+10%)
Vanilla-needy
3,33
3.54
3,65
3.49
3.75
3.57
3.64
3,86
Vanilla samppling
3,34
3.53
3,60
3.48
3,77
3,58
3,65
3.85
VCD
3.46
3.61
3.59
3.96
3.67
3.95
4.21
3.59
3.54
119
Woodpecker
3:37
3.55
343
3,03
3.61
3.99
3,82
3.87
3.03
PAI
3,38
3,56
3,5
3.58
3,89
3.69
3.82
4.04
HALC:
3.7
3.58
3,83
3.71
4.01
3.59
3,80
4.14
VDGD (ours)
3.70 (+5%)
(495))
(.85
(.10()
3.79 (+6%)
4.09 (+ (+125)
4.34
(+125)
Vanilla
2,44
2,66
3.13
2.86
3.35
3.35
3.45
3.64
Vanilla-samplinng
samplling
2,45
2,65
3.10
2.84
3.33
3.37
3,45
3.61
2,20
2.44
2,96
2.67
3,10
3.21
3.30
3.59
OPERA
2,50
2.49
3.20
2.92
3.44
3.42
3.53
3.68
Woosppokker
246
3.70
3.74
2:49
3.55
2,60
3.48
3.60
3.78
232
7.77
374
204
3,50
3,61
VIGGD (ours)
2.78 (6145)
2.84 (4798)
3.49
(+12%)
3.15 (+109)
3,68 (-10%)
3.59 (475)
375 (1-58)
3.92 (4755)
Vanillo-gredy
3.01
3.12
3.23
3.13
4.12
686
4.15
4.37
Vonilla-smmpling
2:08
3.10
5.27
5.17
4.08
3.71
4.17
4.13
VCD
2,90
3.16
3.18
3.15
3.89
3.57
4,13
4.28
OPERA
3,08
3.18
3.30
3.22
4.16
3.75
4.22
4.40)
LLaVA-Bench
Woolppekkrr
3,10
3.22
3,36
3.23
4.21
3.73
4.21
4.39
LRV
3,16
3.19
3,30
3.22
4.27
3.72
4,20
4.49
LURE
3.18
3,26
3.24
4.25
4,27
4.51
3:20
4.54
3:25
3,27
3,37
3.22
3,76
4.35
4.53
VIGD (ours)
3.6(-122))
3.47
(41199)
3.52 (195)
(18R)
(4885)
3.961485)
(33)))
(495)
Vanill-grooly
2.39
3.42
2.72
3.39
Vanilla samplia-
2.34
2.50
2.70
2.84
3:30
3.35
3,42
3,62
VCD
2.29
2.61
2.67
2.92
3.29
3.42
3.39
3.58
OPERA
2.48
2,67
2.77
3.01
3.57
3.52
3.60
MMVET
Woodpeeker
2.47
2.64
2,82
3.06
3.40
3.44
3.50
3.72
LRV
2.58
2,50
2.79
2.92
3.38
3.42
3,51
3.75
LURE
2.54
2,63
2,81
3.12
3.41
3.46
3.56
3.71
PAI
2662
2.60
2,83
3.18
3:37
3.49
3.59
3.78
HALC
266
2,70
333
3.44
3:47
333
3.76
285(CT995)
(-T99)
3.57
(+83))
3.99
Vanill-greedy
1955
2,05
2.20
2.66
3.23
3.45
Samplia-impiin
1.64
2.70
3.21
3.40
VCD
147
1.55
1,30
1,30
2:30
2.63
3.19
3.42
OPERA
2.04
2,05
2,62
2.26
2.65
2.85
3.22
3.47
VaLLa
Woodppokerr
2.01
2.05
2.67
2.23
2.60
2.91
3.28
3.52
LRV
196
2.10
2.65
2.19
2.59
2.58
3.32
3.51
LURE
2,03
2,05
2.64
2.24
2.64
2.78
3.28
3.48
PAI
2.04
2.25
2.78
2.34
2.76
2.86
3,31
3.54
HALC
200
3.22
2.74
233
2.73
2.89
3.29
3.32
VIGD
(
2.64
3.16 (12008)
2.72()248)
3.45 (1305)
(4-88)
(468))
Table 2: Performance comparison of VDGD with various baselines. VDGD outperforms by 2%%-33%
Proposed VaLLu Benchmark. Automatic evaluation of LVLMs, with powerful closed-
source models, on multiple large benchmarks can be prohibitively expensive. Thus, propose ValLu, benchmark that we build by amalgamating existing benchmarks.

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Published as a conference paper at ICLR 2025
VaLLu comprises total of 1500 instances, with 554 sourced from Oven. 535 from MMMU, 218 from MMC, 74 from MathWiita. 60 from Hallu- sionBench, 53 from MATH-Vision and 14 from MME. We only include instances with instruc- tions that require open-ended generations and do not include any binary Yes/No or Multi-choice questions. We control the size to ensure the eval- uation affordable while keeping the diversity of tasks and topics for comprehensive analysis. VaLLu manually filtered for noisy examples commonly found in existing benchmarks (we discuss this further in Appendix K.3), annotated with additional meta-data (illustrated in Fig. 9 and
Science
Business
290 116 8%%)
Coding
271 G24 6%)
Engineering
Maths
Medicine
7at (59.79)
InfoSeek
Visual Perception
InfoSeek-Reasoning
Figure Distribution of task types in VaLLu.
paired with an expert-providde response.
5.3 RESULTS AND ANALYSIS
Quantitative results. Table compares the performance of VDGD with other baselines. As clearly see, VDGD not only outperforms all baselines but is also the only method that alleviates and improves performance together with scores for other aspects beyond factuality, where we show that VDGD does not compromise the depth and expressivity of responses. Ablation results in Appendix C demonstrate that (i) removing KL divergence leads to drop in performance (ii) using only the description prefix without the image leads performance drops confirming that VDGD enhances reasoning through grounding rather than making the model rely completely on the description alone, and (iii) more advanced captioning models can significantly enhance VDGD performance, while smaller captioning models provide competitive results with the added benefit of lower computational overhead.
VaLLu. Results other full benchmarks
provided in Appendix [L.6
Qualitative examples. Fig. 10 compares responses from vanilla greedy decoding with VDGD. Grounding response generation results in more factual responses. Appendix iillusttraes 2 failure cases of VDGD where incorrect descriptions can lead VDGD to hallucinate.
Question: Daxter Company has 2. relevant range of production between 15,000 and 30,000 units. The following cost data repeesents average vuriible costs per unit for 250000 unis of production. If 15,000 units are prodaced, what the variable cost per uui!?
Question: Where typically lound!
per munufactuing overhend) (Variable munifacturing Numberr units) to Total varablee (S10 Sij (53 + 55) (52 15J800) 130.188 Vanialic cod unt Toul vaniable cou/uuneer of
Greedy Decoding: To find
colculate
and divide by
Greedy Decoding: The image shows wasp on yellow flower.
usis prodused Toaal variable (Direct mateials Direct
mataials
50 118150000 - 52.00 Therefere. if 15 200 unis are produced, the varablle cost per unit is (0.
VDGD Docoding: weep typically found meadows, find feed labitats found
VDGD Deceding: fidd vartblle oast
iabl and divide by Variable manifuctaing ing ovedlead cost a Vaiiable sales commissions cood) Nunneer of units, Total varialle cost (59
calcalate
number units produced. Total variable
(Direc labor cost Indirect matenall
= $3 93 1140 100- 500, 'n Viriable cost per unit = Total varable Nomber of units 5950 00000105000000 000 363.3, Therefore, if 15,00 uniss are prodoced. the viriable cost per uit would be $63.33.
settlements if the envtrooment provides suitable nesting sites and sources.
Figure 10: Illustration of responses generated with VDGD compared with greedy decoding. More in Sec. K.2
CONCLUSION, LIMITATIONS AND FUTURE WORK
In this paper, we conduct extensive experiments to analyze hallucinations in LVLMs. Our findings reveal that although there has been considerable progress in reducing hallucinations related to visual recognition, reducing hallucinations for cognitive prompts has been limited. Further investigation into LVLM responses to cognitive prompts reveals a visual perception gap: despite having the required capabilities, LVLMs often produce hallucinations when responding to prompts. To address this issue, introduce VDGD, novel and training-free free method to reduce hallucinations. Our results demonstrate that VDGD is the first method to effectively reduce hallucinations in responses that necessitate additional cognitive skills beyond just visual recognition.
As part of future work, we would like to work on (I) Error accumulation in VDGD that comes from inaccurate image descriptions in the prefix and (2) the requirement of prompting the L.L.M twice.
10

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Published as a conference paper at ICLR 2025
7 REPRODUCIBILITY STATEMENT
We provide our code here: https::/sreyaamn.ggiina github. .o/VDDDD All experimental details, including training parameters and typpe-parraeeters provided in Section 5.2 The Appendix has more comprehensive details about our experimental setup.
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A
APPENDIX
In the supplemental material,
1. We illustrate failure cases for VDGD.
2. We provide results for ablation of VDGDs key components.
3.
D
We provide correlation between GPT scores and actual metrics.
4.
We provide detailed explanation of computational complexity of VDGD algorithm
described in section
5. We describe the impact of the paper on society and research communities.
6. We provide the prompts used for object detection, description, rephrasing, noise removal and evaluations required for our experiments.
7.
We provide detailed description of all the datasets utilized in this paper.
8.
We provide a detailed description of all the models we have experimented with.
9. We provide a detailed description all the prior-art hallucination mitigation techniques
used as baselines.
10.
K
We provide details about:
K.I
We provide detailed explanation about Logit Space for VR and Cognitive Prompts. We provide examples comparing generations from Greedy, Decoding, VCD and
K.2
VDGD.
K K.4
We provide and in-depth explanation of all the details of Algorithm
K.6
We explain the elbow method used for logit analysis.
K.
We provide examples of rephrased prompts which do require image input.
K.
We provide details of computational resources used for this paper.
We provide additional results for all the experiments described paper.
M
We provide details about computation cost for VDGD.
13.
We provide results for VDGD with beam search.
14. We provide some further disceusion about related works.
B
VDGD FAILURE CASES
Fig. illustraate 2 failure cases of VDGD. We show that VDGD lead incorrect responses due to an incorrectly generated image description. However, we also argue that as LVLMs get better at describing images, the performance of VDGD decoding will improve.
Queition: Which unaeal telons clace that isclades munth with her?
stepts)
epproducing?
Dierrppten:
Thr dooked degam choss excletionryy ribiienchie betwrnm voroors pnimushe animall The
The
diagam lllerrinng gronth and ruprodtctionn hh dogmun coneat srrce aurbhrred stope, with each showing lig difficmn entest of of proces. The diggann femuees rumber of cirsko, whish represer. ine buste ocl. le cirilo are placed at vation postionn alens toe diagrtes, indiening the diflrear stgges of the call's prowds and npoodiction dir ar comerted by armoes Enooghh dillsen sagge
Deveription: The
of's bactunal calis
satias a labeled "Lunette"" the scourd abeeed " Laaass and nee thnd ludded d'Faa oction las for aninsss with one the left one the middl, and on the right The thind saction has anmaal,
coittooeny thee rigas snimas clor sisal reprosontion relttonlipe bew these streals, making sakk tedessadd ther
the
mesemunt ofe
stamale Eint sectier, "Lanstte" belong to clade tnciudis
divila Eoeriall cells which
Figure 11: Iuuttration of failure of VDGD.
C VDGD ABLATION RESULTS
To demonstrate the effectiveness of VDGD, we conduct ablation studies on its key components: (i)
VDGD (- VCD): Image descriptions generated without using Visual Contrastive Decoding (VCD),
(ii) VDGD (+ GPT-4 Captions): Image descriptions are generated using GPT-4-turbo-203 couurbo-222--4. 2024-04-09
instead of the L.LM itself. (iii) VDGD (+ Ramos al. Captions): Image descriptions generated
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using the small captioning model introduced by Ramos et al. instead of the LVLM itself. (iv) VDGD (- KLD): KLD-based sampling is removed, and only the description is appended, (v) VDGD Image): VDGD is applied without the original image input; only the image description is appended to the prompt. Results are presented on the VDGD benchmark in Table 3 Removing KI. Divergence leads substantial performance drop. We notice similar phenomenon by removing the input image with the steepest drop. This shows that the input image is still essential for the model.
As can clearly see, VDGD achieves significant performance boost when using captions generated by GPT-4, highlighting the potential of higher-quality captions to enhance VDGD's effectiveness. This is a case of implicit knowledge transfer from GPT-4 VDGD through detailed descriptions. Additionally, VDGD delivers competitive results with small-scale captioning model, illustrating an alternative, more computationally efficient approach improving performance. These findings suggest that future advancements in both large and small captioning models could further enhance the performance and efficiency of VDGD.
Benchmurk
LLaVAw1
LLaVA-I.
LLaVA-1.6
mPLUG-Owl2
InternI.M-X
CogVLM
Vanilla greeedy VDDD (ours]
1.95
2.03
2.6
2,20
2.66
2.82
2.66
2,4
1.72
VDGD (+) GPT- Captions
2.31:ous
2.91.na
3.37
2.97
3.65
3.44
VDGD (+)
Ramas al. Captions
2.00+a..c
2.38+00s
3.00000s
2:43en.0
3723
2.00
VDGD VCD
2.08=0.0
2.43
3:ll
2.54.000
3:20.2.12
2.0520.000
VDGD
2.015..00
1.
2.78
2.37.000
2.87 10.00
VDGD (-) Image
1.692007
1.9540.0
2.43+2.06
1.9820.00
2.2120.04
2.50
Table Ablation results for VDGD. VDGD sees a drop in performance when the KLD decoding objective removed and a major drop when no input image is provided.
D CORRELATION BETWEEN GPT SCORES AND ACTUAL METRICS
Background of Experts in Human Evaluation. For the expert human evaluation of model gen- erations, engaged group of PhD students with least four years of research experience in computer vision. These experts were tasked with evaluating the outputs of the models for accuracy, relevance, and factual correctness. To ensure thorough evaluation, they were provided with internet access to verify factual information when necessary. The evaluation process was conducted in an anonymized format to maintain objectivity, with the experts working independently and without prior knowledge of the model specifics. We selected 500 samples or the size of the benchmark, whichever was lower. Scores presented in Table are averaged across scores provided by the students.
Table 4
presents scores in the format of original benchmark metrics / GPT Scores / Expert Human Scores. Remarkably, we show that our proposed GPT score has a higher correlation to Expert Human Score. Nonetheless, our GPT Score also has high correlation with the original benchmark metrics.
Dataset
Baseline
LLaVA-15
mPLUG-Out2
InternLM-X
CogVLM2
Aug Correlation
Vinilla
/.554 3.91
1450 /9/4.49 3.82
1712.00 3.75/4.15
15/2.45/3.66 4.00
Woodpooker
1503
150888/3.66/3.00
3.82/4.21
4.33
0,55 0.95/0.96/00
VDGD
1696:23/3.00
1724.27
4.12/4.56
Vanilla
52.72/ 1,40//4/22 2,14
MMMU-Val
Woodpecker
1..12/1.44/1.88
36.35 1.11 1,79
47.58/211/2.00
2..8//2.14//.5
0.9/0.96/0.44
VDGD
36.80/1.62/2.33
38.42/1.377 2.55
$3.86/2.39/ /2.80
56.43/2.447 /.16
Vanilla
65.4213.10/3.4 3.49
69:37 /3.17/ 3.00
72.21 74.08 /4.22
11.6//4.17/..99
LLaVA Bench
Woodpecker
70.48/3.22/3.55
70.68 /.233/ 3.34
74.87/421/ /4,44/44448
74.52/4.21 /4.17
0.96 0.977..9700.95
VDGD
71.03/3.47
/3.38
77.43/445/4.77
/4.73
Vanilla
/6./2884/
3..2/33.2
MMVET
Wondpecker
35.42/264/2..6
3.06/ 3.29
65.78 3.500
VIGD
45.19/3.0//3 3.27
62.41 /3.45/ 3,90
/..77 3.78/
71.98 3.74/400
0.97 0.99 00.1
Table 4: Scores across benchmarks in Original Metrie/ (Our proposed) GPT Score (Our) Expert Human Score format. We also show the person correlation averaged across datasets. The correlation scores are in Original Metric-GPT Score / GPT Score-Expert Human Score / Original Metric-Experl Human Score format. GPT Scores show higher correlation with Expert Human Scores.
E VDGD COMPUTATIONAL COMPLEXITY
Given an LVLM with a vocabulary size V, an input instruction with a description length M tokens, each decoding step, the complexity for equation (2) is O(V) and let the number of plausible
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tokens after this step be K, the complexity for equation (3) is ((VMK). The total computational complexity of VDGD at each decoding step is O(V) + +(VVK) = O(VMK).
F BROADER IMPACT
This paper addresses the broader impact of reducing hallucinations in Large Vision-Language Models (LVLMs), which are increasingly utilized in diverse applications such automated content generation, assistive technologies, and hallucination, particularly in responses to cognitive prompts, our work has the potential to enhance the reliability and safety of LVLMS in practical scenarios. The proposed Visual Description Grounded Decoding (VDGD) method, being training-free, offers an accessible and scalable solution that can be readily implemented across various models to mitigate risks associated with inaccurate or misleading outputs. Furthermore, the introduction of the VaLLu benchmark contributes to the field by providing a tool for the comprehensive evaluation of cognitive capabilities in LVL.Ms, promoting transparency and fostering further research in the area. Collectively, these contributions aim to improve user trust in Al systems and encourage responsible Al development that prioritizes factual accuracy and understanding.
systems. By focusing on the phenomenon of
G PROMPTS
For image description benchmarks, we employ the prompt illustrated in Fig. 12 description benchmarks, we modify this prompt slightly (we modify the part of the prompt for extracting hallucinated phrases), and we illustrate the same in Fig. 16 The prompt used to generate image descriptions for VDGD is illustrated in Fig. [5] Correctness in the prompts refers to Factuality in all tables in Section D We name it so after an ablation study on the terminology that works better for prompting the judge (GPT-4). All prompt evaluations on GPT-4 are done with temperature of 0.7.
For non-image
Evaluation Proupt
Tlease act impartial dudge and eretoate the quality of resporsee poorsded wase respect Exput image, zate quality balkocinated contact or vrongful indommation it the cosponse wics caspact to the Arpes inage.
teipfulsess, Correctnens, tepapement. will outpoc
Howergr (natrontionn
Hccurptt canover)
soperts
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eetteserrrrd cupgesto disjalnted confuring denrectrate sualiate the correctseee or accuracy of the cosponte provided with seepect to the caags and the cospective queectirn saked sbout The laage. perfect indiaates the seggasse is ertieely correot and socurate, while ruggeate it has cigni fleanr errore or Far lot question Depth: Seteratne Lorol detatt and thescugheete
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Figure 12: Prompt used to evaluate image description benchmarks using GPT-4-Turbo as described in Section
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I Rephrase Pronpt
vitl proside you wieh reghraro the instrrotion oseh that the agean ie acked to sefeer wore to the image for octoting the instriation to ourpet a faithful rerpoore founcentty it to looking wone at the Enstosetion) Instisotionn (knot)
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Figure 13: Prompt used to evaluate the performance of LLMs on rephrased instructions using GPT-4-Turbo as described in Section 4.2
4 Mealts Prompt
image, image description that conatitute laage. These pizzasen Eacaqround and background objects, advertias phaaase, Recezz coras sepacated vacoes. thice should nos be any adcitional infomation other that thene vacuea in the outpas. the coapinan Ln as follova: (resporse]
you
Figure 14: Prompt used to identify the visual elements in images using Llama3 for the algorithm described in Section 3.3
Description
clinages
have gaves this imaqu to complate the tast described (Asatt..
help complete the teak, direrize the given inaqe is deteil. In of zeot vorid please isclude all tosegreins and backepouudd objecta in the deacription, their propecties clibe oclort shaps, stt-), their culutions with otber sodecti,, theie count, and will othez componenta the Lmape, cuse nun cwel weeud acunee, like chasts, grapto, tablen, abe.. pleuse deszzibe the tublay mantion aid numbwes tuet,
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Figure 15: Prompt used describe images using LVLMs for VDGD as described in Section
Resept
Pleate act at on inpartist tudgo and exstuare the myaliry of the vesposson provlieed wich rerpeet the Eepur image. You will vate the quilryy peapotze miltipas expecca, auch as lelphuaness, Clazssa, Borewesanzz and Depth. fou vili stao outpus Liata wish hellscinased contens "nforration veeponoe
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Figure 16: Prompt used to evaluate generations from cognitive benchmarks in Table 2
H
DATASET DETAILS
VaLLu We propose VaLLu benchmark which is sourced from Oven, MMMU, MMC, Math HallusionBench, MATH- Vision and MME. This dataset is licensed under all the licenses of the original benchmarks that it was sourced from.
AMBER AMBER (Wang et al., 2023a) is a benchmark for evaluating hallucinations in both the generative task and discriminative task of MLLMs. It provides comprehensive coverage of evaluations for various types of hallucination, including existence, attribute, and relation. Licensed under Apache- 2.0 License.
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Bvaluation Recept
please act en on Iepostlel let Judge and outvate the qustity of an mage desert cooeepp forton provided by on coent wich copecty whitch Inferret intrrnttivenee iveness Prentacly, will provide you with on topus Eroney coge Live quer about the topot en egent vinselie or You need fodge informtivv the deseciption is with respect responding correctly the cognitive qaary.
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Figure 17: GPT evaluation prompt for evaluating informativeness of image descriptions generated by M.V.Ms.
Teat-only Rusisation
Ziease ect sa in irpactied tudge und emsturce the quality of taw reapossees proviced, You Mil. cute tow quacitty or the cutpes on muzcapue aegedte, lelgfsliets, clariry, conrectiens, bepels, Rigagnwent.
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Prompt used to evaluate generations for rephrased instructions benchmarks like VaLLu, MMMU, Math Vista, and SynthDoG using GPT-4-Turbo as described Section 4.2
Figure 18:
MMMU MMMU (Yue et al. 2024) benchmark evaluates multimodal models on multi dinlii-dsscipinee tasks and demands college-level subject knowledge and deliberate reasoning. It consists of I 1.5k questions, spanning 30 subjects and 183 subfields, focusing on advanced perception and reasoning on domain-specific knowledge. Licensed under Apache-2. 2.0 License.
MMC MMC (Liu et al. 2023a) is a annotttn benchmark comprising 9 distinct tasks to evaluate LVLM's reasoning capabilities to comprehend visual charts. It comprises tasks such as chart information extraction, chart reasoning, contextual chart understanding, etc. also offers two evaluation methods: GPT4 free-format general ability evaluation and multiple-choice QA format chart understanding. Licensed for rescarch purposes.
Math Vista Math Vista (Lu et al. 2023) a robust mathematical reasoning evaluation benchmark that consists of challenging tasks that require fine-grained, deep visual recognition and compositional reasoning. It consists of 6141 examples derived from 31 multimodal datasets involving mathematics. Licensed under CC-BY-SA-4.0.
MATH-Vision MATH-Vision (Wang al. 2024a) dataset is collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. The dataset spans 16 distinct mathematical disciplines and is graded across 5 levels of difficulty to evaluate the mathematical reasoning abilities of Multimodal Large Language Models(MLLLM). Licensed under MIT License.
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HallusionBench HallusionBench (Guan al. 2023 is a comprehensive benchmark designed for the evaluation of image-conte reasoning. The benchmark presents sigmificant challenges to advanced large visual- -anguagg models (LVLMs), such as GPT--V(ision), Gemini Pro Vision, Claude 3, and LL- 1.5, by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 129 questions, all meticulously crafted by human experts. Licensed under BSD-3-Clause.
Llava Bench LlavaBench (Liu et al. 2023c) is a challenging evaluation suite designed to assess visual-language alignment and cnttructtio-follovviii capabilities of large visual-language models. It consists of two benchmarks: LLaVA L.a-AA-Bend Benchh (COCO), which includes 30 randomly selected images from COCO-Val-22 Val-2014 paired with 90 questions covering conversational, descriptive, and complex reasoning tasks, and (LaVA-BBnnhl (In-the-Wiid featuring 24 diverse images from various domains, including memes, paintings, and sketches, with 60 questions.
MM-Vet MM-Vet etu al. 2023 an evaluation benchmark designed to assess large multimodal models on complex multimodal tasks. It defines six core vision- vision-language Srpabilitis-RReognniin Knowledge, OCR, Spatial Awareness, Language Generation, and Math-and examines their integra- tion across 16 emergent tasks. MM- Vet employs an LLM-based evaluator for open-ended outputs, providing unified scoring across diverse question types and answer styles. The benchmark offers insights into the capabilities of different large multimodal model paradigms and models beyond simple performance ranking.
MME Multimodal large language model Evaluation Benchmark (MME) (Fu et al. 2023) mea- sures perception and cognition abilities on total of 2 tasks(Perception) and Congition) and 14 subtasks(Existencee, Count, Position, Color, Poster, Celebrity, Scene, Landmark, Artwork, OCR, Commonsense Reasoning, Numerical Calculation, Text Translation and Code Reasoning). To prevent data leakage from use of public datasets for evaluation, the annotations of instruction-answer pairs all manually designed.
Oven Open-domain Visual Entity Recognition (OVEN) (Hu et al. needs to link an image onto a Wikipedia entity with respect to a text query. OVEN challenges models select among six million possible Wikipedia entities, making it general visual recognition benchmark with the largest number of labels. Licensed under MIT License.
2023 is task where model
SynthDoG SynthDoG (Kim et al. 2022) is created by generating synthetic data which consists of various components: background, document, text, and layout. The background image sampled from ImageNet, and the document is sampled from the collected paper photos. The text is sampled from Wikipedia. Layout is constructed using randomly stacks grids. The english variant of the dataset consists of 65.5k training and 500 validation entries. Licensed under MIT License.
I MODEL DETAILS
LLaVa-v1. LLaVa-v1 Liu et al. 2023c is an open-source LVLM trained by fine-tuning LLaMA/Vicuna (13B) on GPT-generated multimodal instruction-followinn data. It is initially trained on 558K filtered image-text pairs from LAION/CC/SBU and then, finetuned on 80K GPT-generated multimodal followiiin data. Licensed under CC BY-SA 4.0 DEED.
LLa L.-.Va-1.5 (Liu al. 2023b builds on L.La VL-VV'-V's 's architecture by replacing linear projection design with a two-layer MLP. It also includes additional academic- task-orientedd VQA datasets for VQA, OCR, and region-level perception, to enhance the model's multimodal capabilities. Licensed under CC BY-SA 4.0 DEED.
LLa Va-1.6. LLa 1.6 (Luu et al. 2024c re-uses the pretrained connector of VA- and is instruction tuned on 158K GPT-generated multimodal data, 500K academic-task-orienee VQA data, 50K GPT-4V data and 40K ShareGPT data. I.LaVa-1. 1.6 has improved reasoning, OCR, and world knowledge. Licensed under CC BY-SA 4.0 DEED.
https//huggingglacecom http:/ggggnaaaaccoo////oaaaaaa/a htw.::/lwwee./b--buhiiigiinnnnno
http://huuggnghhccccoollhhaaaaan
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the first Multi-modal Large Language Model that performs well in both pure-text and multi modal scenarios. mPLUG-Owl2 is pretrained 400 million image-text pairs from: Conceptual Captions (CC3M/CCI2M)) COCO, Laio-en, COYO, DataComp and for instruction tuning 5 types of data are used: image captioning, mage question answering, region-aware QA, multi-modal instruct data and text-only instruct. Licensed under MIT License.
mPLUG-OW12. mPLUG-Ow12 YE et al. 2023
In-ernLM-XX. InternLM-X Zhang al. 2023 is vision-language large model based In- te (Cai et al. 2024) for advanced text-image comprehension and composition. InternL is pre-trained 1.1 billion images alongside 77.7 billion text tokens, including both public datasets and in-house concept data followed by supervised fine-tuning using multi-task training and instruction tuning. Licensed under Apache-2.4. 2.0 License.
CogVLM CLgVLM (Wang et al. 2023b is powerful open-source visual language model. Cog VLM-17B has 10 billion vision parameters and 7 billion language parameters. CogVLM pre-trained using 1.5B text-image pairs and instruction tuned on various visual question wnnweerng datasets. LLM achieves statc-of-the-ar performance on 10 classic cross-modal benchmarks. Licensed under Apache-2.0 License.
Llama-2-7b. Llama-2-7b Touvrro et al. 2023 an open source fine-tuned generative text model with billion parameters. It is an auto-regressive language model that uses an optimized transformer architecture. The fine-tuned version use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) for helpfulness and safety. Licensed under Llama 2 community license agreement.
Vicuna-7b-v1.5, Vicuna-7b-vl.5 (Zheng et al. 2023 is 7b parameter open-sourced model and fine-tuned from Llama with supervised instruction fine-tuning and is an auto-regressive language model based on the transformer architecture. The training data is around 125K conversations from ShareGPT. Licensed under Apache-2.0 License.
InternLM2-7B. InternLM-7B (Cai et al. 2024 10
InternLM2 is open-source 7 billion parameter chat model with a 200k context window and achieves good performance NheeNNedl--nn- "nyyee-ei----aystae"""" test(on long-context tasks like LongBench and L-E L-Eval) and is trained using Conditional Online RLHF (COOL RLHF). Licensed under Apache-2.0 License.
VLMV CogVLM2 (Hong et al. 2024 CogVLM2 a new generation visual language models based Meta-LLaMA-3-833 Instruct. offering significant improvements over previous versions. It supports 8K content length, image resolutions up to 1344x1344, and excels in both English and Chinese. VLM2 has achieved state-of-the-art performance benchmarks like TextVQA, DoeVQA, and MM-Vet. The open-source models, including Cog VLM and CogVLMM- Video, are available on GitHub, contributing advancements in image and video understanding. Licensed under Apache-2.0 License.
Qwen2-VL. Qwen2-Vi VL Wang et al. 2024b Qwen2-VL VL is the latest iteration of the Qwen-VL model, featuring state-of-the-art performance in image and video understanding tasks. It excels in processing images of various resolutions and aspect ratios and supports long-form video under- standing for tasks like question answering and content creation. Key architectural updates include Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-ROPE), improving its multimodal processing capabilities. Available in 2, 7, and billion parameter variants. Licensed under Apache-2.0 220 License.
19B
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J BASELINE DETAILS
VCD. Visual Contrastive Decoding (VCD) (Sicong Leng et al. 2023) is a training-free method that contrasts output distributions of original/unalttreed inputs and distorted visual inputs. VCD hypothesizes statistical bias and unimodal priors as the two essential causes of object hallucinations and reduces the dependence on them. This results the generated content being closely grounded to visual inputs resulting more accurate outputs. Licensed under Apache-2.0 License.
OPERA. Over-trust Penalty and a cnrispecctionAlloaattin (OPERA) (Huang et al., 2023b) is training-free approach that hypothesizes that hallucinations are closely tied the knowledge aggregation patterns in the self-attention matrix and that generation of new tokens by models is majorly dependent on few summary tokens(over-trrus issue) but not all of the previous tokens. This methodology introduces a penalty term on the model logits during beam-search decoding to mitigate the over-trust issue and implements a rollback strategy to retrospect the presence of summary tokens. Licensed under MIT License.
2023b) is a training-free method that identifies and corrects hallucinations in the generated text. Unlike other training-frre methodologies. Woodpecker works after the model generation process. This method works by creating visual knowledge base of Question-Answer(MM. pairs which contain object-level and attribute-level claims about the input image followed by an LLM correcting hallucinations.
Woodpecker. Woodpecker (Yin et al.
LRV. LRV (Liu et al. 2024a), or Large-scale Robust Visual Instruction tuning, proposes dataset of visual instructions generated by GPT4, covering 16 vision-and-lant and-language tasks with open-ended instructions and answers. Each instance has positive instruction samples and negative instructions for more robust visual instruction tuning. As a result, a model trained on this dataset proves to be robust hallucinations. Licensed under BSD-3-Clause license.
LURE. LVLM Hallucination Revisor (LURE) (Zhou al. 2023b attributes object hallucination to three factors: co-occurrence, uncertainty, and object position, and develops an object hallucination revisor which converts potential ballucinations into accurate outputs. This method first creates a hallu- cination dataset by making modifications to the original data and trains the object hallucination revisor on this dataset. This method post-hoc rectifies object hallucinations in LVLMs by reconstructing less hallucinatory descriptions.
PAL. Pay Attention to Image (PAI) (Liu et al. 2024d) is a training-free algorithm designed to reduce hallucinations in LVLMs. PAI intervenes during the inference process make it more image- centric by amplifying attention to image tokens in the self-attention layers of LVLMs. By adjusting attention weights and subtracting logits of text-only inputs from multi-modal inputs, PAI mitigates the issue of "text inertia," where outputs are overly influenced by context text. This method enhances image representation during generation, leading to more accurate, visually grounded outputs without requiring additional training or external tools.
HALC. HALC (Chen et al., 2024a) is novel decoding algorithm designed to mitigate object halluci- nations (OH) in LVLMs. It integrates robust auto-focal grounding mechanism to correct hallucinated tokens locally and specialized beam search algorithm to reduce OH globally while preserving text generation quality. HALC can be easily integrated into any LVLM without additional training, addressing all three types of OH OH-exxitence, attribute, and relationship. Extensive experiments show that HALC outperforms state-of-the-aa methods across multiple benchmarks.
K ADDITIONAL DETAILS
K.1 LOGIT SPACE FOR VR AND COGNITIVE PROMPTS
From our in-depth analysis of the logit space, we that language hallucinations in LVLMs have a unique characteristic - the probability of the most confident token in the vocabulary marginally decreased from the probability of the same token in the base LLM when prompted with the same prior context (the most confident token by the LVLM will also be the most confident token by the language-only base I.LM the token language hallucination). Thus, decoding -hased mitigation
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methods like OPERA and VCD benefit from this unique property, and contrasting tokens help mitigate
hallucinations.
K.2 QUALITATIVE COMPARISON OF GREEDY VS VCD vs VDGD DECODING
In this subsection, we illustrate several instances (figures 20 their responses for LLa with Greedy, VCD, and VDGD Decoding.
24
from VaLLu and compare
Question: Baxter Company
relevant range
production betwoon 15,000 and 30,000 unils. The following cost data represents avenage variable costs per unit for 25,000 units of productionn. If 15,000 units are produced, what is the variable per unit?
Question: Where
it typically found?
Part
Greedy Decoding: To find the variable cost per unit, need calculate the total variable cost and divide it by the number units produced. 'n Total varialle cost (Direct materials Dirret libor] (Indirect materials' Fixed munufacturing overhead) (Variable manufacturing Nuerhesd"Numbet of units). 'n Total variable cast (S10 59) (S3 S6) (52 * 15,000) per
Greedy Decoding: The imagee shows a wasp on yellow flower.
$30,118, 'n Vanable 52.00. Therefore, if 15,000 are produced, variable cost per $2.00.
Total varialle cost/Number of 5m1ts=50,,188135.000
VDGD Decoding: The is typically found in gandens, and other habitats where it can rind flowers to feod on. It may also be found human seillements enviroement provides suitable nesting sites food sources.
unii, need to calculate the tocal variable (Direct labor and divide the number of produced. in Total vanialle Variable sales commissions Indirect materials cost Variable manufheturing overhend
VDGD Decoding: To find the variable
uumberr units. Total variable cast (59 varialle cost/aimbrr
$144 000 $950,000. $450,000/15000
the
Variable per unit- Therefore, 1,,000 units produced, the variable per would be $63.33.
563.33 13.
The image is a table that lists the everage cost per unit for various production-eeittd expenses. The table has to columns: one for the type of expense and another for the average cost per unil. The oy of expenses listed are direct materials. direct labor, indiredt materials, fixed manufacluing overhead, variable manufacturing overhead, fixed selling and administrative expenses, and vanable sales commissions. ThenThe average cost per unt for each expense is as follewsin- Direct matarials: $10in- Direct labor: Siin- Indirect materials: S3in- Fixed manufacturing overhead: $6nn- Vartable manufacturin overhead: S2in- Fieed selling and administrative expenses: S8in Variable sales commissions: $14minThere are other objects text in the image.
Description:
The image depicts a wasp perched on yellow flower. The wasp brown and black in color, with its wings spread out
Description:
sits the flower. There are several other yellow flowers in the background. creating vibrani scene.
Figure 19: Qualitative example for VaLLu benchmark also showing the descriptions.
The pressure of sample of gas is measured at sea level with closed-end manometer. The liquid in the manometer is mercury. Determine the pressure of the gas in torr.
Question:
Closed end
Gas
The image shows a closed-end manometer with a silver tube and mereury-filled gauge. The manometer measuring the pressure of a gas, specifically, the pressure of gas sea level. The tube is connected the manometer, and the pressure is indicated by the height of the mercury in the tube. The manometer is measuring the pressure in torr, which is a unit pressure. The image provides clear visual representation of the manometer and its function in measuring gas pressure.
Greedy Decoding:
26.4 cm
The pressure of a sample of gus is measured at sea level with closed-end manometer filled with mercury. The manometer has scale that measures the pressure units of torr. The manometer connected to the gas sample through a metal tube that has a closed end to prevent the mercury from leaking out. The manometer is placed in vacuum chamber to minimize external influences on the measurement. The pressure reading shown on the manometer scale, which is typically logarithmic scale that allows for wide runge of pressure measurements to be shown in compact space. The manometer is useful tool for measuring the pressure of gases in various applications, such as scientific research and industrial processes.
VCD Decoding:
VDGD Decoding: The image shows a closed-end manometer with a metal tube containing mereury liquid, measuring the pressure of gas at sea level, The tube is connected to a metal pipe with a scale on it, indicating the pressure torr units. The manometer placed on white background, making it the main focus of the image. The pressure of the gas is 26.4 torr.
Figure 20: Qualitative example for VaLLu benchmark.
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Question:
What is the net sales figure for Stanley Black and Decker?
Greedy Decoding: The net sales figure for Stanley Black and Decker is $10.5 billion.
VCD Decoding: The net sales figure for Stanley Black and Decker is S1.5 billion in 2010.
VDGD Decoding: The net sales figure for Stanlcy Black and Decker is $13.3 billion in 2010.
Figure 21: Qualitative example 2 for VaLLu benchmark.
Question: Dose response data was collected during the preclinical testing of four drugs for the treatment of acute heart failure. Which drug studied was the most efficacious?
Greedy Decoding: Based on the dose-response data, the most efficacious drug studied was the one with the highest efficacy. In the image, there are four different colored lines representing the efficacy of the four drugs. The line with the highest efficacy is one that has the most significant effect on the outcome. This in information be used determine which drug is the most effective in treating acute heart failure.
30a
30*
Log [Drug]
VCD Decoding: The most efficacious drug studied was the one with the highest log dose (log D). Log dose is measure of the concentration of substance in the blood relative to the concentration in the liver, which is an important factor in determining the efficacy of drug. this case, log dose for the green line higher than the log dose for the other lines, indicating that the green line represents the most effective drug among the four studied for the treatment of acute heart failure.
VDGD Decoding: Based the image, the most efficacious drug studied was D. The gruph shows that the dosc-response relationship for D was the steepest, indicating that the drug had the highest efficacy at lower doses. This suggests that D may have more potent effect on acute heart failure treatment compared to the other three drugs, A, B, and C.
Figure 22: Qualitative example 3 for VaLLu benchmark.
D
Question: Three identical rectangles are put together to form rectangle ABCD, as shown in the figure below. Given that the length of the shorter side of each of the smaller rectangles feet, what is the area in square feet of rectangle ABCD?
Greedy Decoding: The area of rectangle ABCD is 12 square feet.
B
To find the area of the rectangle formed by the three identical rectangles, need to find the length and width of each rectangle and then multiply them together. In the image, the length of the smaller side of each rectangle is given as 5 feet. Since there are three identical rectangles, we can find the total length by adding the lengths together: Length (3 x 5 feet) 15 feet. Now we need to find the width of cach rectanglc. Since the length is the same for all thrce rectangles, we can find the width by dividing the length by the number of rectangles: Width = Length Number of Rectangles 15 feet / == feet. Now we have the dimensions for each rectangle: length is 15 feet and width is 5 feet. To find the area of the rectangle formed by these three identical rectangles, we multiply the length and width together: Arca Length x Width 15 feet x feet = 75 square feet. So the area in square feet of the rectangle formed by the three identical rectangles 75 square feet.
VCD Decoding:
VDGD Decoding: Given that the length of the shorter side of each of the smaller rectangles is 5 feet, the area in square feet of rectangle ABCD can be calculated by multiplying the length the longer side by the width the shorter side. The length the longer side 10 feet, and the width the shorter side is feet. Therefore, the area of rectangle ABCD is 50 square feet.
Figure 23: Qualitative example 4 for VaLLu benchmark.
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Question: Where can the animal be found?
Greedy Decoding:
The animal, duck, can be found in body of water, specifically lake.
The animal can be found in the wild, specifically in the tropical rainforests of Central and South America. is also found in some parts of Africa and Asia. The animal is water and vegetation, such as rivers and swamps. It is also known to be found in some parts of the United States and Canada, but these populations are generally small and isolated. The animal is also found in some parts of the world where it is kept in captivity, such as zoos and wildlife parks.
VCD Decoding:
typically found in areas with plenty
The animal can be found in body of water, possibly a lake or a pond, surrounded by other birds.
VDGD Decoding:
Figure 24: Qualitative example for VaLLu benchmark.
K.3 NOISY EXAMPLES IN EXISTING DATASETS
To maintain high quality of the benchmark and eliminate noisy examples, we performed manual review of the existing datasets. This was crucial to ensure that the data used in our paper was reliable. was also essential for the validity of our research findings. We removed questions that did not have a definite ground truth answer. We also made sure that the questions were answerable using combination of general knowledge and reasoning. This filtering was done by the paper authors and has been approved by our institution's 's Institutional Review Board (IRB). We share a few examples
(figures: 25 26
28 29
30
32) of noise in the existing datasets below:
Question: You are a consultant to large manufacturing corporation that is considering project with the following net after-tax cash flows (in millions of dollars).
Company
Discount Store
Everything
Forecasted return
12%
11%
Standard
Besa
1,0
The question asks for the net present value of project and provides specific financial parameters such as project beta, risk-free rate, and expected market return. However, the image provided shows information about two different companies, including their forecasted returns, stundard deviations of returns, and betas, which are unrelated to the specific project's cash flows mentioned in the question. The image neither shows the net after-tax cash flows for the project in question nor any relevant data to perform a net present value calculation based on project's described financial conditions. Thus, image does not support answering question adequately.
Sanity Rationale:
Figure 25: Noisy example for MMMU benchmark.
Question: Fig. 8.15 shows the distribution of 12 spot heights with regular 20 spacing covering rectangular area which is to be graded to form horizontal plane having an clevation of 10,00 Calculate the volume of the earth.
24,99
The image provides the elevations at various points and the distances between these points, but does not provide enough information calculate the volume of earth needed grade the area horizontal plane elevation of 10.00 m. The calculation of volume would require additional information such as the method of interpolation between the spot heights and the be
Sanity Rationale:
27776
Fig. 8.15
specific carthwork calculation techniques to he used. Since the question definitively answered solely with the provided image and general knowledge, the response is no.
Figure 26: Noisy example for MMMU benchmark.
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Question: What is the current employment rate in the UK's travel and tourism sector?
the travel and tourives Industry worldride
The question inquires about the current employment rate the UK's travel and tourism sector. The provided image, however, shows data from 2019 and does not specify whether the numhers represent rates absolute job counts, nor does it give any indication of the employment rate. Additionally, the image historical and may not accurately reflect current employment situation. Therefore, the image does not provide the necessary information answer the question about the current employment rate.
Sanity Rationale:
Figure 27: Noisy example 1 for MMC benchmark.
Question: Did the population of 40-59 year olds change from 2016 2020?
Total population from 2010 2020 age group millioss
The image provided is solid color block without any distinguishable data, axes, or markers that would allow for the evaluation of population changes over time. Therefore, it is impossible to answer the question about the change in the population of 40-59 year olds from 2016 2020 using this image.
Sanity Rationale:
Figure 28:
Noisy example 2 for MMC benchmark.
Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end. Question: What the age gap between these two people in image? (Unit: years)
Question:
The question asks the age gap between two people shown in the image, but it's impossible to accurately determine their ages just by looking at the photograph. Age estimation from photograph without additional information such as the date the photograph was taken or personal details about the individuals highly speculative. Therefore, the question does not meet the condition of having definite, ground truth answer.
Sanity Rationale:
Figure 29: Noisy example for Math Vista benchmark.
Hint: Please answer the question requiring Doating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end. Question: In the figure, what is the magnitude of the potential difference across the $20:Omega$ resistor? Answer in unit of W sig fig.).
Question:
W
LIV
202
Sanity Rationale: The question asks for the magnitude the potential difference across resistor using unit of 'W' which stands for watts, unit of power, not potential difference. The correct unit for potential difference should be volts (V). Therefore, the question contains a fundamental error in its request for unit, making impossible to answer as stated.
W
W
Figure 30: Noisy example 2 for Math Vista benchmark.
Question: According to the positive sequence of the images, are they skating right? Answer one word.
The question asks whether the individuals in the image skating the right, based the sequence of images. However, the images do not show any skating activity. The sequence depicts two individuals performing stunt or trick where one person runs and jumps over another lying on the ground. There are skates visible, the activity shown does involve skating. Therefore, the question not answerable from the image it assumes incorrect context and activity that is not depicted.
Sanity Rationale:
Figure 31: Noisy example for HallusionBench benchmark.
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Question: If ungle BAF equals angle CAF, CF equal to BF? Please answer
A
yes or no.
Based on the image provided. the question asks whether CF is equal to BF given that angle BAF equals angle CAF. The image shows a triangle ABC with a point F on line segment BC and other lines and points within the triangle. However, the image does not provide any explicit information or annotations (like length markings or angle measurements) that confirm whether line segments CF and BF are equal, nor does the condition of BAF equaling CAF automatically imply that CF equals BF without additional context or geometric properties being specified (such as triangle ABC being isosceles with AB AC). Therefore, the question cannot be definitively answered just by examining the image and upplying basic geometric reasoning.
Sanity Rationale:
D
E
M
B
F
Figure 32: Noisy example for HallusionBench benchmark.
K.4 ADDITIONAL DETAILS ON ALGORITHM
In this Section, describe our algorithm verbally.
Token Distribution Analysis. We perform the
analysis proposed by (Lin et al.
2023). Specifically, for a given instraction-respons--nnee
triplet, the instruction i = (41, i2. and the image u is first input to the aligned (or fine-tuned) model to obtain its response r= (ri, r2.'..
via greedy decoding. Next, for each position & in the response, 'context' at this position is defined as to be 2₁ = i + [ri, Tt- J. This "context" is then input to the base model (model from which the aligned model was instruction-tuned, to obtain its probability distribution for predicting the next token at position f, Pawse We then define Base Rank as follows: Rank in Pose of the token at t with the maximum Palign value. With the base rank denoted as 17. the unshifted, marginal and shifted tokens are defined as when (1) 1). (1 < S 3) and (1) > 3) respectively.
Obtaining Hallucinated Phrases. Fig. 12 illustrates the prompt used for AMBER evaluation, where an LVLM is prompted for a description of an input image. In addition to scores, our judge, GPT-4, also returns the exact ballucinated phrases by the LVLM. We divide these hallucinated phrases into 3 types: Object Hallucinations - There are hallucinated visual elements which are objects. Relation Hallucinations- - Action/Verb Hallucination - Hallucinated visual elements that are not objects but just actions or verbs, e.g., walking, etc. Relation Hallucination - Hallucinated visual elements that define relationships (spatial or other kinds) between objects.
Filtering visual elements in Hallucinated Phrases. We prompt LLaMa-3 with the image description output by the LVLM, to extract the phrases from the image description that are visual elements. This ensures filtering out stop- words and all other words that are not visual elements and thus aids our algorithm in getting a precise count of each category of hallucination.
Causal Categorization of Hallucinations. Next, based on the Base Rank formulation and the exact hallucinated phrases detected by our judge (GPT-4), we propose an algorithm (Algorithm []] to automatically categorize identified hallucinations into the 4 different categories. Precisely, for every hallucinated phrase returned by the judge, first check if the word in the phrase is visual element. We do this using the list of visual elements output by LLaMa-33. Next, for the first token of each word of the hallucinated phrase, we check the Base Rank of the token.
1. If the Base Rank is O, we attribute the phrase to be a language hallucination. This is as simple as the phrase forced to be generated by the base L.M itself without looking the image.
2. Next, if the Base Rank is more than 0 and the hallucination can be found in the top-k retrieved elements from the IT dataset the model was aligned using, we attribute it to be an IT hallucination (Ghosh et al., 2024). To retrieve top-k iageeinsttuuction pairs that are closest to the prompt image, we employ CLIP (Radford et al., (021) similarity. Specifically, we calculate the similarity between the image embedding of the image associated with the input prompt and the text embedding of the descriptiionrrespnse associated with the IT instances. Next, for every prompt, we retrieve the top-25 IT instances and search the hallucinated phrase returned by the judge (in descriptiins/responnes using string matching. We show how varying values of & change the final counts for our hallucination categories in Appendix K.5
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3. Next, if the Base Rank is more than O, the hallucination is not an IT hallucination and the hallucination is a Relation Hallucination, we attribute it to be a style hallucination. LVLMs fine-tuned on datasets synthesized from stronger closed-source LVLMs generally learn to mimic their style. One such style is adding more details to an identified object, by relating it to other objects or world knowledge. Our in-depth analysis shows that LVLMs, striving for lengthier, more detailed responses to imitate style, hallucinate and generate wrongful relations for identified objects. Our relation hallucinations returned by the judge precisely capture this, and thus we attribute relation hallucinations with a base rank of more than 0 to be Style hallucinations.
4. Finally, if the Base Rank is more than O, the hallucination is identified as an Obejct hal- lucination by the judge, we attribute be a Vision Hallucination. These are caused by ineffective recognition of objects in the image (due to low-resolution images, weak encoder, etc.)
K.5 RESULTS FOR VARIOUS VALUES OF k FOR IT HALLUCINATIONS
Fig. 33 shows that even for higher and lower values of k, our hypothesis and claims stay unchanged as changing the value of k does not affect the overall frequency distribution of categories.
Figure 33: Frequency comparison of hallucination categories for top-k=10,25,40
K.6 ELBOW METHOD FOR LOGIT ANALYSIS
The Knce/Elbow Detection Method operates by plotting the cumulative distribution of the sorted probabilities and seeking the point that maximizes the distance from the line connecting the first and the last points of this cumulative plot. Conceptually, this corresponds to finding the point of maximum curvature on the plot, indicative of the most pronounced change in the distribution's 's progression. offers robust approach by identifying the point in sorted probability vector where the rate of decrease in probabilities shows a significant bend or "elbow". This method is particularly useful when the probabilities are distributed such that the top few are similar and significantly higher than the rest, which then decrease sharply. The method proves to be effective in experiments with multinomial sampling from a distribution, where it allows us to focus on the probabilities that correspond to the tokens that most likely to be selected by sampling. This method ensures that the chosen cutoff k dynamically adjusts to the specific characteristics of each probability distribution encountered, aligning with the overarching goal of maintaining sampling relevance and computational efficiency.
K.7 EXAMPLES OF REPHRASED PROMPTS
To determine whether LVLM hallucinations stem from the limitations of the base LLM or from alignment issues, we ask GPT-4 to rewrite the instructions in prompts in such a way that the base LLM can answer the question without any image input as described in section We share a few of rephrased prompts in the existing datasets below:
examples (figures: 34
35 36
38
39 40
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The composition of the Fingroup Fund portfolio is as follows: The fund has not borrowed any funds, but its accrued management fee with the portfolio manager currently totals $30,000. There are million shares outstanding. What is the net asset value of the fund?
Question:
Stock
Shares
Price
A
200,000
$35
300,000
40
400,000
600,000
25
Rephrased Instruction: A portfolio consists of four types of stocks. Stock has 200,000 shares priced at $35 each, Stock B has 300,000 shares priced at $40 each, Stock C has 400,000 shares priced at $20 each, and Stock D has 600,000 shares priced at S25 each. The fund has not borrowed any funds, but has an accrued management fee $30,000. There are million shares outstanding. Calculate the net asset value per share for this fund.
Figure 34: Rephrased prompt example for VaLLu benchmark.
Question:
After school, Maya and Naomi headed to the beach, miles away. Maya decided to bike while Naomi took bus. The graph below shows their journeys, indicating the time and distance traveled. What was the difference, in miles per hour, between Naomi's and Maya's average speeds?
Maya
Dittane
Maya and Naomi traveled to a destination 6 miles away. Maya biked and completed the journey in 30 minutes. Naomi took bus and completed the journey in 10 minutes. Calculate the difference, in miles per hour, between Naomi's and Maya's average speeds.
Rephrased Instruction:
15 Time (mimmote))
Figure 35: Rephrased prompt example for VaLLu benchmark.
Question: If this area was selectively destroyed on both sides, could vomiting be induced by either pharyngeal tickling gastric distress?
Rephrased Instruction: If the area of the brainstem that includes the dorsal motor nucleus the vagus nerve (marked as in typical diagram) and the area postrema (often indicated with an near the fourth ventricle in diagrams) were selectively destroyed on both sides, could vomiting still be induced by pharyngeal tickling or gastric distress?
XXI
Figure 36: Rephrased prompt example for MMMU benchmark.
Question: Suppose a three-factor model is appropriate to describe the returns of stock. Information about those three factors is presented in the following: What is the systematic risk of the stock return?
Expected Volue
Volue
0000734
$17.004
$7784
Inflcion
2.80%
2.4%
Intarent rates
-32
1.2%%
15%
Rephrased Instruction: Given three-factor model with the following factors and their respective beta values: GDP with beta of 0. 00000734 Inflation with beta of -0.90 0.90, and Interest rates with beta of -0.32, can you calculate the systematic risk of the stock return?
Figure 37: Rephrased prompt example for MMMU benchmark.
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Published as a conference paper at ICLR 2025
Question: Hint: Please answer the question requiring an integer
Most preferred objects
different categories
answer and provide the final value, e.g,, 1, 2, at the end. Question: How many objects preferred by more than people in at least category?
Rephrased Instruction: Consider a bar graph with five objects labeled as veto, saw, calf, oxen, and fusion, each represented in two categories: grease and picnic. The number of people who prefer each object each category ranges from to 10. How many objects are preferred by more than 5 people in at least one of these categories?
Mumbbr of People
greee
Figure 38: Rephrased prompt example for Math Vista benchmark.
Question: Hint: Please answer the question requiring a floating-point number with decimal places and provide the final value, e.g., 1.23, 1.34, 1,45, at the end. Question: what the difference between the largest and smallest number of cases?
Given the number of cases in thousands for various cities: Quezon City has 106.71 thousand cases, Cavite has 72.34 thousand cases, City of Manila has 66.88 thousand cases, Laguna has 58.31
Rephrased Instruction:
thousand cases, and Rizal has 55.57 thousand cases. What is the difference between the largest and smallest number of cases, expressed as a Mloating-poin number with two decimal places?
Figure 39: Rephrased prompt example for Vista benchmark.
Question: which year were these orange objects first used in BCE?
Rephrased Instruction: In which year were terracotta roof tiles first used in BCE?
Figure 40: Rephrased prompt for SynthDoG benchmark.
Question: This animal can be mostly found in which region?
Rephrased Instruction: In which region can the Ugandan Kob, a type of antelope, be mostly found?
Figure 41: Rephrased prompt 2 for SynthDoG benchmark.
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Published as a conference paper at ICLR 2025
K.8 COMPUTE INFRASTRUCTURE
All our analysis, inference and baseline experiments are conducted on node of 4 NVIDIA RTX A6000 GPUs, with 128GB RAM and 10 CPU cores. The evaluations are conducted using gpt-4- turbo-2024-04-09 model.
L ADDITIONAL RESULTS
L.1
COMPARISON OF VARIOUS LVLMS ON REPIRASED PROMPTS
Table 5 provides quantitative values for rephrased prompts without image input for various LVLMs,
detailed in section 4.2 of the main paper.
Dataset
Model
Helpfulness
Clarity
Factuality
Depth
Engagement
LLaVA-v1.5-t
3.05
4.02
2.83
2.70
3.85
VaLLu
mPLUG-OW12-t
3.70
4.15
3.14
3.11
3.24
InternLM-X-t
4.28
4.58
3.66
3.62
3.57
LLaVA-v1.5-t
1.97
3.25
1.69
1.87
3.25
MMMU
mPLUG-Ow12-t
2.48
3.05
1.63
2.22
2.64
InternLM-X--
3.51
3.93
2.43
2.98
3.01
LLaVA-v1.5-t
2.53
3.58
2.21
2.10
3.44
Math Vista
mPLUG-OWI2-t
3.56
4.05
3.04
2.52
2.79
IntemLM-X-
4.39
4.65
3.99
2.91
2.95
LLaVA-v1.5-t
3.38
4.48
2.95
2.32
3.35
Oven
mPLUG-OWI2-L
3.62
4.56
2.99
2.47
3.37
IntemLM-X-t 3.80
4.58
3.76
2.89
3.52
Table 5: Performance comparison of various LVLMs on rephrased prompts without images as described in Section
L.2 COMPARISON OF VARIOUS LVLMS ON VALLU BENCHMARK WITH NO IMAGE INPUT.
providds comparison of various LVLMs on VaLLU benchmark when prompted with no image but image descriptions are provided.
Methodology
LLaVA-11
LLaVA-v1.5
LLaVA-v1.6
mPLUG-OW2
InternLM-XX
CogVLM
Vanilla-greedy
1.67
1.78
2.07
1.76
2,02
2.23
VCD
1.31
1.40
1.42
2.04
2.21
OPERA
1.65
2.29
1.91
2.30
2.44
Woodpecker 1.77
1.85
2.29
2.02
2.41
2.60
Table 6: Comparison of LVL.Ms performance when prompted without an image but image descriptions provided VaLLu benchmark. The values correspond to the "Factuality" metric.
L.3 COMPARISON OF VARIOUS LVLMS ON IMAGE DESCRIPTION
Table 7 provides quantitative values for faithful image description generations for various LVLMs on multiple benchmarks, detailed in section 4.2 of the main paper.
L.4 COMPARISON OF LVLMS ON DIFFERENT CAPABILITIES ON THE PROPOSED VALLU BENCHMARK
Table provides comparison of L.L.Ms different capabilities the proposed VaI.Lu benchmark.
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Dataset
Model
Helpfulness
Clarity
Factuality
Depth
Engagement
LLaVA-v1
3.79
4.46
3.2
3.27
0,96
1La
4.29
4.73
3,91
3.44
1.26
6
4.30
4.78
3.85
4.29
1.27
AMBER
mPLUG-Owl2
4.02
4.58
3,48
3.27
0.71
InternLM-X
4.59
4.86
4.37
4.00
1,40
CogVLM
4.68
4.93
4.49
3,96
1.28
GPT-4-Turbo
4.89
4.96
4.79
4.64
1.50
LLaVA-vl
1.11
2.09
1.11
1.14
0.47
1..
1.07
2.24
1.17
1.15
0,48
1LaVA-1.6
2.51
2.92
2.63
1.99
1.11
OCR
mPLUG-Owl
1.21
1.15
1.16
1.10
0.64
InternLM-X
1.11
1.12
1.12
1.03
0.67
CogVLM
3.12
3.04
2.68
2.53
1.47
GPT4-Turbo
4.15
4.02
3.78
3.98
1.67
LLaVA-vI
1.77
2.73
1.50
1.65
0.46
1L.
2.45
3.34
2.00
2.23
0,59
LLa
3.54
3.98
2.95
3.39
0,99
MMMU
mPLUG-Ow12
2.94
3.64
2.56
2.33
0.82
InternLM-X
3.60
4.10
3,24
3.15
1.05
Cog VLM
4.03
4.38
3.57
3.43
1.03
GPT-4-Turbo
4.86
4.92
4.73
4.56
1.15
LLaVA-v1
1.95
2.84
1.95
1.85
0.76
LLaVA-1.5
2.57
3.47
2.23
2.36
0.98
LLa
3.68
4.07
3.17
3.48
1.12
MathVista
mPLUG-Ow12
3.14
3.85
2.78
2.46
1.03
InterLM-XX
3.91
4.32
3,45
3.35
1,23
CogVIM
4.20
4.52
3.78
3.66
1.24
GPT-4-Turbo
4.54
4.97
4.12
4.10
1.35
LLaVA-v1
1.81
2.90
1.51
1.75
0.56
LL5
2.66
3.49
2.21
2.42
0.71
LLaVA-1.6
4.09
4.32
3.50
3.90
1.09
MMC
mPLUG-Ow12
3.63
4.15
3.32
2.60
0.97
IntemLM-XX
3.72
4.17
3.28
3.25
1.10
CogVLM
4.27
4.58
3.91
3.92
1.32
GPT-4-Turbo
4.81
4.89
4.57
4.49
1.12
Table Performance comparison of various LVLMs on popular benchmarks for image description. While AMBER has real-world scenes, others do not. Experiment described in Section 3.2
Methodology
VP
VP+Info-Seek VP+hnfö--Sek=RRessonnng
VP+Reasoning
LLaVA-v1
1.62
2,60
1.67
1.70
LLaVA-v1.5
2.59
3.18
1.50
1.76
LLaVA-v1.6
3.46
3.68
1.67
2.15
mPLUG-Ow12
2.56
3.23
1.67
1.94
InternL
3.75
3.90
2.33
2.56
CogVLM
3.00
3.37
1.25
2.08
Table 8: Comparison LVLMs on different capabilities proposed VaLLu benchmark "Factuality"
metric.
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L.5 COMPARISON OF POST-TRUNCATION PROBABILITY STATISTICS ON AMBER AND MMMU BENCHMARKS
Table 9 provides post-truncation probabilities for all LVLMs AMBER and MMMU, this is extension of the statistic shown in section 4.3 of the main paper.
Dataset
Model
k
Variance
Range
Avg.
LLaVA-v1
4.19
0.05
0.31
0.23
LLaVA-v1.5
3.91
0.07
0.39
0.29
VLa
3.78
0.08
0.43
0.32
AMBER
mPLUG-Ow12
3.65
0.06
0.36
0.28
InternLM-X
3.56
0.09
0.44
0.36
CogVLM
3.16
0.91
0.47
0.38
LLaVA-vI
3.68
0.08
0.39
0.27
LLaVA- 1.5
3.53
0.09
0.41
0.23
LLaVA-v1.6
3.40
0.09
0.42
0.24
MMMU
mPLUG-Ow12
3.32
0.10
0.44
0.26
InternL--X
3.26
0.14
0.46
0.28
CogVLM 3.16
0.19
0.47
0.25
Table 9: Post-truncation probability statistics for various models using the elbow method. All values are computed across all hallucinated tokens in each dataset.
L.6 COMPARISON OF VALLU WITH MITIGATION BASELINES
Table 2 provides quantitative values for comparison of VDGD and other baselines the VaLLu benchmark, detailed in section 5.3 of the main paper.
L.7 COMPARISON OF MMMU WITH MITIGATION BASELINES
Table 11 provides quantitative values for comparison of VDGD and other baselines on the MMMU benchmark, detailed in section 3.2 of the main paper.
L.8 COMPARISON OF MATHVISTA WITH MITIGATION BASELINES
Table 12 provides quantitative values for comparison VDGD and other baselines on the Math Vista benchmark, detailed in section 3.2 of the main paper.
L.9
COMPARISON OF OVEN WITH MITIGATION BASELINES
Table 13 provides quantitative values for comparison of VDGD and other baselines on the Oven benchmark, detailed section 3.2 of the main paper.
M COMPUTATIONAL EFFICIENCY
Table 14 presents computational analysis of various methods evaluated on LLaVA 1.5 using 48GB GPU, comparing their average inference time and computational cost measured in teraFLOPs
(T). While the VDGD method requires more computation than the baseline methods, the increase modest and within a reasonable range when considering the significant performance gains it offers. Specifically, VDGD has an average inference time of 2.5 seconds and a computational cost 11.9 teraFLOPs, which is manageable increase compared to the Vanilla-greedy method's 1.3 seconds and 9.3 teraFLOPs. Similarly, the combination of VDGD with a Small Captioning Model Ramos al. (2023) demonstrates acceptable computational demands with inference time of 2. seconds and 1.4 These results suggest that the enhanced performance of VDGD methods is achieved with computational requirements that well within practical limits, making it viable option.
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Model
Methodology
Helpfulness
Clarity
Factuality
Depth
Engagement
Vanilla- greedy
2.61
3.69
1.95
1.92
1.25
Vanilla-sampling
2.64
3.73
1.86
1.94
1.27
VCD
1.70
2.55
1.47
1.81
1.59
LLaVA-vI
OPERA
2.60
3.75
2.04
1.89
1.30
Woodpecker
2.68
3.81
2.01
1.95
1.28
LRV
2.64
3.78
1.98
1.94
1.36
LURE
2.60
3.54
1.89
1.40
VDGD (ours)
2.38
3.39
2.16
2.46
1.34
Vanilla-greedy
2.64
3.72
2.03
2.03
1.25
Vanilla-sampling
2.57
3,65
2.01
1.92
1.22
VCD
1.94
2.99
1.55
1.65
1.05
LLaVA-1.5
OPERA
2.73
3.74
2.05
2.04
1.22
Woodpecker
2.74
3.87
2.05
2.03
1.29
LRV
2.76
3,82
2.10
2.03
1.28
LURE
2.72
3.76
2.03
2.05
1.27
VDGD (ours)
2.97
3.91
2.64
2.27
1.54
Vanilla-greedy
3.08
3.81
2.63
2.67
1,43
Vanilla-sampling
3.10
3,85
2.64
2,63
1.40
VCD
2.01
2.96
1.80
2.10
1.20
LLaVA-1.6
OPERA
3.10
4.01
2.62
2.60
1.42
Woodpecker
3.16
4.00
2.67
2.58
1.44
LRV
3.15
3.98
2.65
2.62
1.39
LURE
3.11
4.02
2.64
1.48
VDGD (ours)
3.51
4.04
3.16
2.87
1.58
Vanilla-greedy
2.73
3.64
2.20
1.96
1.28
Vanilla-sampling
2.72
3.60
2.18
1.91
1.24
VCD
2.16
2.95
1.80
1.85
1.26
mPLUG-Ow12
OPERA
2.92
3,96
2.26
2.08
1.34
Woodpecker
2.89
3.90
2.23
2.02
1.24
LRV
2.90
3.94
2.19
2.05
1.28
LURE
2.92
3,89
2.24
2.06
1.32
VDGD (ours)
3.14
3.98
2.72
2.22
1.50
Vanilla-greedy
3.11
3.98
2.66
2.34
1.47
Vanilla-sampling
3.13
4.04
2.70
2,41
1.56
VCD
2.56
3.26
2.32
2.19
1.41
InternLM-XX
OPERA
3.14
4.20
2.65
2.32
1.46
Woodpecker
3.13
4.12
2.60
2.28
1.41
LRV
3.06
4.19
2.59
2.31
1.45
LURE
3.12
4.10
2.64
2.40
1.49
VDGD (ours)
3,57
4.37
3.45
2.65
1.68
Vanilla-greedy
3.25
4.19
2.82
2,43
1.55
Vanilla-sampling
3.26
4.21
2.83
2.40
1.55
VCD
2.80
3.62
2.63
2.10
1.58
CogVLM
OPERA
3.40
4.27
2,85
2,46
1.57
Woodpecker
3.44
4.30
2.91
2.45
1.59
LRV
3.42
4.26
2,88
2.42
1.54
LURE
3.39
4.20
2.78
2,41
1.52
VDGD (ours)
3.33
4.15
3.01
2.51
1.60
GPT-4-Turo
4.02
4.44
3.63
2.94
1.59
Table 10: Performance comparison of various LVLMs on VaLLu benchmark. This is an extension of results shown in Table
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Model
Methodology
Helpfulness
Clarity
Factuality
Depth
Engagement
Vanilla- greedy
1.81
2.79
1.36
1.51
1.00
Vanilla-sampling
1.82
2.82
1.38
1.56
1.06
VCD
1.70
2.67
1.22
1.40
0.95
LLaVA-vI
OPERA
1.88
2.87
1.45
1.61
1.07
Woodpecker
1.89
2.88
1.48
1.60
1.06
LRV
1.83
2.85
1,40
1.57
1.08
LURE
1.91
2.90
1.52
1.63
1.09
VDGD (ours)
2.09
2.99
1.64
1.74
1.14
Vanilla-greedy
1.86
2.90
1.47
1.70
1.09
Vanilla-sampling
1.83
2.88
1.42
1.67
1.08
VCD
1.70
2.52
1.34
1.58
0.97
LLaVA-1.5
OPERA
2.18
3.08
1.62
1.75
1.22
Woodpecker
2.15
2.93
1.57
1.63
1.18
LRV
2.16
2.88
1.60
1.67
1.16
LURE
2.03
2.87
1.46
1.71
1.15
VDGD (ours)
2.30
3.17
1.85
1.86
1.20
Vanilla-greedy
1.85
2.67
1.54
1.80
0.97
Vamilla-sampling
1,80
2.63
1.50
1.78
0.96
VCD
1.68
2.57
1.36
1.68
0.94
LLaVA-1.6
OPERA
1.86
2.70
1.57
1.84
0.99
Woodpecker
1.90
2.74
1.63
1.89
1.04
LRV
1.88
2.72
1.61
1.85
1.02
LURE
1.92
2.73
1.65
1.86
1.03
VDGD (ours)
2.11
2.89
1.81
1.92
1.05
amillla-greed
2.24
3.12
1.58
1.65
1.16
Vanilla-sampling
2.25
3.12
1.59
1.66
1.14
VCD
2.10
2.98
1.42
1.56
1.04
mPLUG-Ow12
OPERA
2.35
3.23
1.67
1.74
1.18
Woodpecker
2.28
3.17
T.6T
T.69
1.15
LRV
2.34
3.24
1.66
1.72
1.14
LURE
2.32
3.2T
1.63
1.67
1.19
VDGD (ours)
2.48
3.36
1.82
1.78
1.23
Vanilla-greedy
2.95
3.71
2.14
2.39
1.63
Vanilla-sampling
2.97
3.76
2.17
2.45
1.64
VCD
2.76
3.54
2.03
2.26
1.57
InternLM-X
OPERA
3.09
3,86
2.20
2.43
1.66
Woodpecker
3.07
3.83
2.19
2.46
1.68
LRV
3.11
3,88
2.24
2.49
1.70
LURE
3.10
3.89
2.22
2.48
1.71
VDGD (ours)
3.21
4.11
2.57
2.78
1.79
Vanilla-greedy
2.42
3.34
1.69
1.83
1.22
sailliasampling
2.44
3.37
1.68
1.84
1.23
VCD
2.22
3.15
1.53
1.68
1.17
VLM
OPERA
2.56
3.47
1.76
1.88
1.27
Woodpeecer
2.57
3.47
1.78
1.90
1.26
LRV
2.58
3.49
1.80
1.92
1.25
LURE
2.54
3.42
1.75
1.85
1.22
VDGD (ours)
2.65
3.56
1.92
1.97
1.29
GPT-4-Turbo
4.15
4.53
3.66
2.99
1.95
Table 11: Performance comparison of various LVLMs on MMMU benchmark (only questions tagged with open-ended generation). This is an extension of results shown in Figure
N VDGD WITH BEAM SEARCH
VDGD can be seamlessly applied to beam search decoding without modifying its core methodology: Before of tokens is selected for each beam, VDGD reweights the logit space of the current
35

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Published as a conference paper at ICLR 2025
Model
Methodology
Helpfulness
Clarity
Factuality
Depth
Engagement
Vanilla- greedy
3.74
4.54
2.23
2.28
1.42
Vanilla-sampling
3.72
4.56
2.24
2.27
1.43
VCD
3.54
4.32
2.05
2.07
1.36
LLaVA-vI
OPERA
3.80
4.63
2.29
2.35
1.50
Woodpecker
3.78
4.60
2.26
2.30
1.47
LRV
3.76
4.64
2.30
2.38
1.49
LURE
3.74
4.59
2.25
2.28
1.46
VDGD (ours)
3.92
4.78
2.52
2.42
1.54
Vanilla-greedy
3.65
4.38
2.36
2.01
1.32
Vanilla-sampling
3.68
4.39
2.38
2.04
1.36
VCD
3.38
4.09
2.35
1.87
1.10
LLaVA-1.5
OPERA
3.74
4.46
2.26
1.93
1.32
Woodpecker
3,46
4.53
2.34
1.97
1.38
LRV
3.53
4.42
2.39
1.96
1.27
LURE
3.33
4,41
2.37
1.91
1.29
VDGD (ours)
3.84
4.61
2.56
2.15
1.45
Vanilla-greedy
3.72
4.38
2.50
2.34
1,50
Vanilla-sampling
3,69
4.32
2.45
2.28
1.48
VCD
3.54
4.11
2.34
2.18
1.35
LLaVA-1.6
OPERA
3.80
4.47
2.59
2.46
1.57
Woodpecker
3.78
4.42
2.36
2,43
1.33
LRV
3.75
4.40
2.54
2.42
1.52
LURE
3.79
4.41
2.52
2.41
1.53
VDGD (ours)
3.90
4.53
2.79
2.51
1.58
amiilla-gredd
3,79
4.43
2.48
2.08
1.33
Vanilla-sampling
3.80
4.45
2.51
2.12
1.34
VCD
3.62
4.27
2.35
1.98
1.24
mPLUG-Ow12
OPERA
3.85
4.52
2.57
2.20
1.40
Woodpecker
3.84
4.56
2.60
2.24
1.42
LRV
3.82
4.54
2.59
2.22
LURE
3.86
4.58
2.61
2.25
1.45
VDGD (ours)
3.99
4.65
2.76
2.36
1.52
Vanilla-greedy
4.02
4.69
2.84
2.35
1.55
Vanilla-sampling
4.01
4.68
2.37
1.54
VCD
3.82
4.37
2.66
2.10
1.44
InternLM-X
OPERA
4.13
4.76
2.92
2.42
1.59
Woodpecker
4.07
4.72
2,40
1.58
LRV
4.10
4.73
2.89
2.40
1.56
LURE
4.14
4.75
2.91
2.43
1.60
VDGD (ours)
4.28
4.89
3.12
2.54
1.66
Vanilla greedy
3.72
4.39
2.76
2,63
1.44
Maiila--sapliigg
3.73
4.38
2.74
2.61
1.42
VCD
3.58
4.27
2.64
2.50
1.37
CLM
OPERA
3.76
4.45
2.82
2,69
1.47
Woodlpeckrr
3.75
4.44
2.79
2.64
1.45
LRV
3.80
4.51
2.86
2.70
1.49
LURE
3,82
4.53
2.88
2,73
1.52
VDGD (ours)
3.94
4.64
2.99
2.80
1.60
GPT-4-Turbo
4.05
4.75
3.17
2.24
1.61
Table 12:
Performance comparison various LVLMs on Math Vista benchmark (only questions tagged with open-ended generation). This is extension of results shown in Figure 7
logit based on the KL-Diver KL-Divergence between the current logit and the image description logits. This process is repeated at every decoding step. Another important point to note is that VDGD operates independently of prior response tokens, relying solely the fixed image description tokens, which
36

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Published as a conference paper at ICLR 2025
Model
Methodology
Helpfulness
Clarity
Factuality
Depth
Engagement
Vanilla- greedy
3.12
4.41
2.44
2.19
1.23
Vanilla-sampling
3.14
4.42
2.45
2.20
1.26
VCD
2.98
4.10
2.20
2.07
1.16
LLaVA-vI
OPERA
3.20
4.48
2.50
2.22
1.28
Woodpecker
3.21
4.49
2.46
2.21
1.25
LRV
3.25
4.51
2.51
2.23
1.29
LURE
3.20
4.47
2.49
2.20
1.28
VDGD (ours)
3,42
4.59
2.78
2.30
1.30
Vanilla-greedy
3.25
4.41
2.66
2.61
1.19
Vanilla-sampling
3.24
4.40
2.65
2.58
1.10
VCD
2.82
4.10
2.44
2.39
1.20
LLaVA-1.5
OPERA
3.22
4.40
2.49
2.58
1.18
Woodpecker
3.26
4.42
2.46
2.64
1.22
LRV
3.25
4.38
2.52
2.59
1.24
LURE
3.24
4.35
2.51
2.60
1.2I
VDGD (ours)
3.47
4.56
2.84
2.51
1.80
Vanilla-greedy
3,43
4.41
3.13
3.31
1.35
Vamilla-sampling
3,40
4.38
3.10
3,27
1.30
VCD
3,26
4.19
2.96
3.13
1.20
LLaVA-1.6
OPERA
3.49
4.48
3.20
3.38
1.40
Woodpecker
3,45
4.45
3,21
3,35
1.42
LRV
3.42
4.40
3.18
3.34
1.39
LURE
3.38
4.36
3.18
3,28
1.36
VDGD (ours)
3.64
4.71
3.49
3,42
1.45
Saiilla-greddy
3.23
4.42
2.86
2,48
1.25
Vanilla-sampling
3.24
4.47
2.84
2.50
1.24
VCD
3.07
2.67
2.40
1.17
mPLUG-Ow12
OPERA
3.29
4.52
2.92
2.58
1.27
Woodpecker
3.27
4.50
2.90
2.52
1.25
LRV
3.23
4.49
2.50
1.24
LURE
3.26
4.51
2.91
2.51
1.28
VDGD (ours)
3.34
4.64
3.15
2.70
1.32
Vanilla-greedy
3.28
4.52
3.35
2.59
1.37
Vanilla-sampling
3.27
4.53
3.33
2.57
1.35
VCD
3.08
4.24
3.10
2.44
1.30
InternLM-X
OPERA
3.37
4.65
3.44
2.65
1.43
Woodpecker
3.38
4.63
3.45
2.66
1.44
LRV
3.40
4.69
3.47
2.70
1.46
LURE
3.36
4.64
3.42
2.68
1.42
VDGD (ours)
3,54
4.78
3.68
2.79
1.49
Vanilla-greedy
3.58
4.51
3.35
2.76
1.38
Sailll-sampling
3.59
4.50
3.37
2.78
1.41
VCD
3.36
4.32
3.21
2.58
1.30
CLM
OPERA
3.68
4.60
3.42
2,84
1.45
Woodlpeckrr
3.70
4.58
3.45
2.85
1.42
LRV
3.65
4.57
3.40
2,82
1.40
LURE
3.72
4.62
3.46
2,83
1.41
VDGD (ours)
3.89
4.82
3.59
2.89
1.49
GPT-4-Turbo
4.15
4.79
3.89
3.25
1.67
Table 13:
Performance comparison of various LVLMs on Oven benchmark. results shown in Figure 7
This is an extension of
remain constant during beam search. As result, VDGD can be directly integrated into beam search decoding to achieve similar improvements in reducing hallucinations. Table [5] shows the result of VDGD with beam search MMMU dataset.
37

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Published as a conference paper at ICLR 2025
Method
Average Inference Time (s)
FLOPs (T)
Vanilla-greedy
1.3
9.3
VCD
1.9
10.2
VDGD Small Captioning Model
2.1
1 11.4
VDGD
2.4
11.9
Table 14: Comparison of average inference time and computational cost (in teraFI.OPy, among VDGD and baseline methods evaluated LLaVA 1.5 using 48GB GPU.
Benchmark
LLaVA-vI
LLaVA-1.5
Vanilla-greedy
1.26
1.35
Vanilla-sampling
1.27
1.44
VDD
1.34
1.52
OPERA
1.30
1.43
Woodpecker
1.32
1.44
LRV
1.29
1.49
LURE
1.31
1.47
PAI
1.42
1.39
HALC
1.40
1.54
VDGD
1.42
1.62
VDGD Beam Search
1.39
1.58
Table 15: Performance comparison of VDGD w/ beam search for LLaVA-v1 and LLaVA-1.5
FURTHER DISCUSSION ON RELATED WORKS
Recent advances in understanding hallucinations Large Vision-Language Models (LVLMs) provide critical insights into addressing the persistent challenge of hallucination in multimodal systems. Bai al. (022)) highlight the growing importance of mitigating hallucinations in LVLMs, particularly in tasks involving complex interactions between visual and textual data. Their comprehensive survey dissects hallucinations into granular categories, such object attributes and relations, and identifies factors contributing to hallucinations across data, model architecture, and inference stages. Liu et al. (2023c) similarly focus on categorizing and analyzing hallucinations, emphasizing the importance of detailed evaluation metrics and benchmarks tailored to measure both generative and discriminative capabilities LVLMs. They propose frameworks for systematically assessing hallucination symp- toms and mitigating them through refined model alignment and enhanced multimodal representation. These studies underscore the necessity of bridging gaps in current LVLM evaluation methods and mit- igation strategies by addressing unique challenges such data quality, alignment module efficiency, and decoding mechanisms. Our proposed VDGD model aligns with these efforts by introducing a robust framework to reduce hallucinations, leveraging state-of-the-ar insights while contributing novel mitigation pathways. This connection to the broader context enriches our understanding and opens pathways for integrating these findings into future iterations of VDGD and related LVLM frameworks.
38


Reviews:
[{"review_id": "Tbpoasi8ys", "reviewer": "Reviewer_9PSE", "summary": "In this paper, the authors argue that the current LVLMs and hallucination mitigation decoding strategies lack visual perception capabilities. Through extensive analyses, the authors delve into such deficiency across cognitive and real-world benchmarks and categorize more detailed hallucination taxonomies beyond mere object hallucination: Language Hallucination / Vision Hallucinations / Style Hallucinations / IT Hallucinations. At the end, the authors introduce a new decoding strategy, VDGD, which prefixes the model's detailed descriptive response to the given input and truncates grounding of its intermediate responses to refine the final answers.", "questions": "1. How can hallucinatory results be mitigated using the proposed VDGD in the newly defined hallucination categories, compared to other decoding strategies? Analyses through section 3 and 4 are really intriguing, but the reviewer belives that there is significant gap to bridge such motivation and findings into the VDGD design.\n\n\n2. The method of VDGD is limited to merely prefixing self-generated model response and relying on the first generated response that the model predicts (ironically this may include a lot of hallucinatory responses-even if limitation section mentioned this). Considering LLMs are more prone to hallucination snowballing rather than error correction, it is unclear where the performance gains are derived from. Unlike original contrastive decoding, VDGD cannot make logit corrections by counteracting with premature models and relies solely on vocabulary truncation.\n\n\n3. Computational analyses should be conducted such as throughput or latency. Also, can this VDGD be seamlessly applied to beam search decoding? Then, how will be the result comparison?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Through section 3 and 4, in this paper, the authors extensively explored hallucination issues across various benchmarks, models, and decoding strategies. The novel taxonomies beyond simple object hallucination are crucial to understand the current problems in hallucination research areas (particularly LVLM).", "weaknesses": "Even with the new hallucination categories, and new findings, their approach, VDGD, lacks of analyzing its effectiveness on the new hallucination categories they defined and limited for its computational costs due to successive response generation.", "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": "1ZKBRY69Ad", "reviewer": "Reviewer_fYgL", "summary": "This paper focuses on the cognitive reasoning aspect of hallucination in large vision language models. Through a set of analysis and experiments, it demonstrates that the core blocker of this issue is the difficulty of linking recognized visual concepts to the internal knowledge of LLM. Therefore, the paper further proposes a simple method that per-appendes the image description to the text instruction as the full instruction so that the model can better leverage its reasoning capacity. Evaluation shows that this method can achieve consistent performance improvement on reasoning-focused benchmarks.", "questions": "Please see weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The problem of cognitive reasoning in hallucination is interesting and seems to be under-explored in previous works.\n2. The analysis is sufficient, solid, and easy to follow, yielding several interesting insights.\n3. The proposed method, although appears to be very simple, is based on the analysis on \"language grounding\" in previous sections, which has a reasonable motivation of such design.\n4. The method demonstrates consistent improvements across benchmarks.", "weaknesses": "1. This method seems to be limited in science domain, e.g., chart understanding and reasoning. The underlying assumption of the method is that the image can be sufficiently described by texts. It might hold true for science images, e.g., one can easily describe a chart by enumerating all the involved data or simply transforming the chart figure into a table. However, for natural scenes with complex object categories, attributes, and relations, it is almost impossible to fully represent the image with texts. The evaluated benchmarks seems to be focused on such kind of data.\n2. Based on my first point, I may suspect that the essential reason of the performance improvement comes from that chart figure is more intuitive for human eyes while text descriptions of data is more suitable for LLM to understand. It may has little relation with **cognitive reasoning**.\n3. Also, based on my first point, we'd better not simply regard such science data as reasoning, there can be other forms of reasoning in natural scenes according to some related works [1].\n4. The analysis, though informative, takes too much space, and it may have overlap with previous works [2]. For example, categorization of hallucination types in this paper is essentially based on the **cause** of hallucination, which has been discussed in previous works.\n5. Moreover, the the experiments and investigation of the proposed method seems to be limited. It is better to involve more ablation studies.\n6. The related works is somehow limited. I understand it might be constrained by the space, but it's important to review and discuss related works about hallucination, reasoning, benchmarks, and so on [2] [3].\n\nI will put my initial score as 5 and I hope the authors can resolve my concerns.\n\n[1] Lai et. al. LISA: Reasoning Segmentation via Large Language Model\n\n[2] Bai et. al. Hallucination of Multimodal Large Language Models: A Survey\n\n[3] Liu et. al. A Survey on Hallucination in Large Vision-Language Models", "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": "9sdPYiH1AI", "reviewer": "Reviewer_cozw", "summary": "This paper addresses the problem of hallucinations in LVLMs. Strictly speaking, the authors provide a way to understand the root cause of such hallucinations in LVLMs. They claim that existing approaches for hallucination mitigation only focus on the visual recognition aspect of the model, and do not dive further into understanding whether the model actually has cognitive skills, thus failing to mitigate hallucinations properly from such models. The authors first conduct a study to investigate the various causes of hallucinations in LVLMs. Then they introduce VDGD, a training-free method to mitigate said hallucinations from an LVLM, and finally propose VaLLu, a benchmark to evaluate cognitive reasoning capabilities of LVLMs.", "questions": "1. In Sections 3.1 and 3.2, you mention that existing hallucination mitigation techniques improve performance on AMBER but not on other datasets. Could you provide quantitative results for this to show this?\n\n2. In Section 3.3, the algorithm indicates that a base rank less than zero signifies a language hallucination. Since ranks are typically non-negative, could you explain how a rank can be less than zero in this context?\n\n3. The algorithm seems to classify instances as information transfer (IT) hallucinations first, which might influence the distribution of hallucination types. How do you ensure that this approach doesn't skew the results, particularly the higher incidence of IT hallucinations compared to visual and style hallucinations?\n\n4. In Section 4.1, you observe a decline in rank as the text prompt lengthens, suggesting reduced attention to image context. Given that longer prompts naturally introduce more textual context, how do you differentiate between the model's reliance on textual versus visual information in this scenario?\n\n5. Your proposed GPT-based evaluation metric shows a correlation of 0.96 with human responses, while existing benchmarks have a correlation of 0.92. Considering this marginal difference, what advantages does your metric offer over traditional evaluation protocols?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1) The paper is well written and nicely presented.\n2) The authors present a classification of different types of halluciantions\n3) The authors recognize the gap between visual recognition capability of a model and ability to utilize it for cognitive tasks\n4) The authors propose a training-free strategy to mitigate hallucinations", "weaknesses": "1) Sections 3.1 and 3,2 are not that informative about the failure of hallucination techniques on cognitive tasks. The claim in 3.1 that all methods boost performance on AMBER but not on other datasets is weak since even on AMBER, the relative performance increase is small, which is the same for other datasets as well. This goes against the claims of these two sections.\n\n2) In section 3.3, in the algorithm, firstly, currently it says that it is a language hallucination if base rank is less than 0. How is a rank less than zero? I think it should be language hallucination if it does not fall inside the visual elements of the response. Moreover, I think you are using GPT-4 vision, and not just GPT-4 for this? Also, the visual content extraction itself will have hallucinations due to use of llama-3. Further, pushing everything first to IT hallucination definitely skews the outputs of visual and style hallucinations, it can be both. And so the results showing huge IT hallucination compared to the other two is misleading.\n\n3) Experiment on 4.1 showing fall of rank as length of text prompt increases is nice, but it is also bound to happen since the textual context is getting added. This does not definitively prove that no image context is being attended to. Also, the rank difference between the two datasets is just 1. \n\n4) The gpt-type metric the authors propose is claimed to have high correlation with the human responses. But in appendix we see that the other correlations are also quite high, with a normal benchmarks having a 0.92 correlation compared to author's 0.96. This marginal difference is not significant enough to claim for a new type of evaluation protocol.", "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": "gJoilmJz85", "reviewer": "Reviewer_4B1M", "summary": "The paper presents a comprehensive and extensive analysis on object hallucination. It proposes VDGD, a method that generates descriptions first, which are then used as prompts for a second inference. During decoding, KLD is calculated with the pre-generated descriptions to identify highly deviant candidates. The authors curate several datasets and introduce the VaLLu benchmark for a comprehensive hallucination evaluation.", "questions": "### Q1. While this does not affect my score, I believe the terms “perception” and “recognition” should be interchanged in the paper. Perception refers to the basic observation of visuals, while recognition is a more complex process based on what has been perceived. However, the paper appears to use these terms in reverse, which could cause some confusion for readers. \n\nRef: https://en.wikipedia.org/wiki/Visual_perception", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "### S1. The paper is well-structured and comprehensive, providing a smooth overall flow.\n\n### S2. The analysis is in-depth and offers interesting insights.\n\n### S3. The VaLLu benchmark has the potential to serve as a comprehensive benchmark for evaluating hallucinations in models.", "weaknesses": "### W1. While the idea is simple and effective, a significant drawback is the latency. The proposed method requires generating long descriptions even for short responses (e.g., in Fig. 8, a single token output would typically be very fast in a baseline method, but the proposed method is much slower). Therefore, it is important to include a latency analysis (e.g., average inference time, throughput) compared to simpler decoding-based hallucination mitigation methods [1,2,3] and baseline LVLMs, especially since many recent training-free methods perform a single inference.\n[1] Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models, Arxiv 2024.  \n[2] Seeing is Believing: Mitigating Hallucination in Large Vision Language Models via CLIP-Guided Decoding, Arxiv 2024.  \n[3] RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in LVLMs, Arxiv 2024. \n\n### W2. Using the description from the first inference as a prompt for the second inference appears to act like a form of Chain-of-Thought (CoT) prompting. The authors should explicitly discuss how VDGD relates to or differs from Visual CoT methods [4,5,6], and compare several aspects (e.g., performance, computational requirements, applicability to different types of tasks). Comparisons or references to recent Visual CoT methods will strengthen the paper.\n[4] Compositional Chain-of-Thought Prompting for Large Multimodal Models, CVPR 2024.    \n[5] Beyond Embeddings: The Promise of Visual Table in Visual Reasoning, EMNLP 2024.   \n[6] Visual Evidence Prompting Mitigates Hallucinations in Multimodal Large Language Models, OpenReview.  \n\n### W3. While each individual analysis is interesting, the overall flow feels somewhat disjointed. The paper presents a categorization of hallucinations and provides extensive explanations for each category, but there are no corresponding experimental results or analysis showing how the proposed method specifically addresses or improves each of these hallucination categories. This lack of connection between the categorization and the method’s effectiveness on each type of hallucination weakens the coherence of the paper. The authors should include a specific analysis or set of experiments demonstrating how VDGD performs on each category of hallucination they've identified. This would help tie together the theoretical framework and the practical application of their method.\n\n### W4. While the in-depth analysis is appreciated, the paper sometimes feels overloaded with content, which can distract from the core focus. At times, it is difficult to follow, and the connection between earlier sections and the methodology feels weak. The dense content also limits the space for method-related experiments, with only one experiment table included in the main paper. Most experiments have been relegated to the appendix, suggesting the need for better content management.", "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)