From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty

## Abstract

Abstract Large language models (LLMs) often exhibit undesirable behaviors, such as hallucinations and sequence repetitions.
We propose to view these behaviors as fallbacks that models exhibit under uncertainty, and investigate the connection between them.
We categorize fallback behaviors — sequence repetitions, degenerate text, and hallucinations — and extensively analyze them in models from the same family that differ by the amount of pretraining tokens, parameter count, or the inclusion of instruction-following training.
Our experiments reveal a clear and consistent ordering of fallback behaviors, across all these axes:
the more advanced an LLM is (i.e., trained on more tokens, has more parameters, or instruction-tuned),
its fallback behavior shifts from sequence repetitions, to degenerate text, and then to hallucinations.
Moreover, the same ordering is observed throughout a single generation, even for the best-performing models; as uncertainty increases, models shift from generating hallucinations to producing degenerate text and then sequence repetitions.
Lastly, we demonstrate that while common decoding techniques, such as random sampling, might alleviate some unwanted behaviors like sequence repetitions, they increase harder-to-detect hallucinations.

## 1 Introduction

Figure: Figure 1: When language models face uncertainty, they exhibit fallback behaviors, shifting from hallucinations to degenerate text generation (repeating previous facts in different phrasing) and finally verbatim repetitions.
Refer to caption: x1.png

Figure: Figure 2: Larger Pythia models resort to more complex fallback behaviors. Models with larger parameters count produce more correct facts (green) and hallucinations (orange) while less repeating facts (blue). The green line indicates the number of ground truth answers.
Refer to caption: x2.png

While large language models (LLMs) have been known to generate human-like language remarkably well (Radford et al., ; Brown et al., ; Touvron et al., ; OpenAI et al., ),
there are growing concerns about their propensity for undesirable behaviors, such as degenerate(^1^11Degenerate text includes repetitive textual patterns and/or rephrasing of previously generated text.) or repetitive text (Holtzman et al., ), hallucinations (Ji et al., ; Zhang et al., ), and verbatim recollection of training samples when processing out-of-distribution inputs (Nasr et al., ).
Previous work (Kim et al., ; Snyder et al., ) has studied these phenomena, and suggested solutions, but has done so for each in isolation, without considering the interactions between them.

In this work, we propose that the behaviors illustrated in Figure 1 can be viewed collectively as *fallback behaviors*, which emerge when the model faces uncertainty.
We aim to categorize and analyze the relationship between these behaviors across a range of LLMs.
To this end, we create controlled yet natural settings to manage the level of uncertainty, force the generation of factual information, and evaluate the correctness of each prediction.
We then test three model families—Pythia (Biderman et al., ), Llama 2 (Touvron et al., ), Llama 3 (AI, ), and OLMo (Groeneveld et al., )—considering various factors that could influence the emergence of different fallbacks: (a) number of parameters, (b) number of pretraining tokens, (c) instruction-following training, and (d) decoding algorithms.
We observe a clear ordering in the appearances of different fallbacks, as demonstrated in Figure 2, which persists across all the above factors.
Our findings points to repetitions as the simplest fallback, followed by degenerate text, and finally hallucinations as the most complex behavior.

We present evidence that increasing the strength of a model tends to shift its fallback behavior to more complex forms, whereas weaker models rely on simpler behaviors like repetitive text.
We demonstrate that the so-called strength of a model can be the result of increasing the model’s parameter count, additional pretraining, or the addition of an instruction-following training phase.
Moreover, we show that as the generation length grows and the model struggles to generate additional factually correct responses or convincing hallucinations, it shifts backwards in the fallback behaviors spectrum from hallucinations to repetitions.

We further examine the effect of the decoding scheme, and demonstrate that while random sampling may alleviate
degenerate text (Holtzman et al., ), it increases the rate of hallucinations, which are harder to detect and potentially more damaging to the user.
Despite evidence suggesting that models may be partially aware of their knowledge gaps (Kadavath et al., ),
models are not easily steered away from non-factual generations.
Even when provided with demonstrations on how to abstain rather than hallucinate, models continue to produce hallucinations.

Overall, our study shows that while increasing models’ strength improves their accuracy (Kaplan et al., ),
the root causes of fallback behaviors persist even in the largest and best-trained models.
Moreover, superficial patches such as decoding schemes fail to solve the problem and can trade easily-detected degenerate text for more nuanced and potentially harmful hallucinations.

To summarize, our main contributions are: (a) A controlled analysis of LLM behaviors under uncertainty, showing that strengthening models increases hallucinations over degenerate text and repetitions, (b) demonstrating that during generation, models undergo a phase shift from correct answers to hallucinations and then repetitions, (c) evidence that methods to reduce text degeneration, like sampling, increase errors and hallucinations.
Our code and data is available at [https://github.com/Mivg/fallbacks](https://github.com/Mivg/fallbacks).

## 2 Fallback Behaviors

Since their emergence as powerful generative tools (Radford et al., ; Brown et al., ), there have been numerous reports of unwanted behaviors of LLMs. Holtzman et al. () showed that greedy decoding methods can cause degenerate generations, such as incoherent text or repetitions.
They propose the use of nucleus sampling to mitigate this problem.
In another study, Nasr et al. () demonstrate that when given out-of-distribution inputs, models might output their training data verbatim.
More recently, growing evidence (Zhu et al., ; Zhang et al., ; Aichberger et al., , inter alia) suggests that even the best models often generate convincing yet factually incorrect text, typically referred to as “hallucinations” or “confabulations” (Ji et al., ; Bang et al., ).
Xiao and Wang () investigate model uncertainty, defining it as the probabilistic uncertainty in predicting the next token, which is influenced by the model’s training and knowledge, and identify it as a cause of hallucinations.(^2^22While the model may experience internal uncertainty when predicting the next token, this might not manifest in the final logits. Once a fallback behavior is selected, it may promote the token, resulting in very high logit value.)

In this paper, we propose to view these seemingly independent phenomena as fallback behaviors that models adopt when uncertain, and we investigate the relationships between them.
Furthermore, we hypothesize that the strength of an LLM influences its fallback behavior, and aim to understand what factors determine how a model would behave in cases of uncertainty.

Specifically, we focus on the following phenomena:
1. Sequence repetition When models face an inability to produce plausible continuations, they tend to repeat previously generated sequences, which are known to be plausible within the current context.
2. Degenerate text As shown by Holtzman et al. (), models can generate degenerate text that is not strictly repetitive but follows a consistent pattern, such as enumerating or repeating sentences with variations in subject entities or attributes.
3. Hallucinations In cases that require the recollection of facts which the model cannot produce, it may fabricate coherent and seemingly plausible yet factually incorrect or unfounded content. We note that creating convincingly plausible incorrect facts is not an easy feat; even in humans, this ability develops at a late stage of development (Talwar et al., ; Evans and Lee, ).

In our analysis, we additionally looked for cases of verbatim recollection of training samples Nasr et al. (), but found no evidence of such fallback behavior in our setting (see further discussion in Section 6.1).

## 3 Experimental Setting

To directly investigate the relation between fallback behaviors and their contributing factors, our setup exposes models to naturally-occurring controlled cases of uncertainty while recalling facts.

#### Datasets

**Table 1: Example questions and answers for our main datasets. ^∗ Taken from Wikipedia at [https://en.wikipedia.org/wiki/Harrison_Ford](https://en.wikipedia.org/wiki/Harrison_Ford). ^♯ While not listed in Amouyal et al. (), Ryoichi Ikegami also wrote and drew Spider-Man: The Manga, according to [https://en.wikipedia.org/wiki/Ryoichi_Ikegami](https://en.wikipedia.org/wiki/Ryoichi_Ikegami).**
| Dataset | Question | Example Answer |
| --- | --- | --- |
| Triv | The following 25 colors are in the Olympic rings\n 1. | Blue, Yellow, Black, Green, Red |
| Bio | The following is a bio of Harrison Ford:\n Harrison Ford | Harrison Ford (born July 13, 1942) is an American actor. He has been a leading man in …^∗ |
| Qamp | The following 25 manga were drawn by Ryoichi Ikegami:\n 1. | Heat, Mai, the Psychic Girl, Wounded Man, Sanctuary, Crying Freeman, Strain^♯ |
| FQamp | The following 25 manga were drawn by Haru Tanemura:\n 1. | N/A (Haru Tanemura is not a known manga creator) |

We use the following datasets:

- 1.
TriviaFacts (Triv): We manually curate 95 high-quality domain-diverse questions with a list of answers (list questions)
that (a) contains multiple elements, but no more than 15, (b) requires only knowledge that appears very frequently in the web, and (c) includes short elements without multiple correct phrasings.
Due to its high-quality and highly controllable setup, we base the bulk of our experiments on this dataset, with minor modifications described inline as relevant to each experiment.
- 2.
BioGeneration (Bio): To investigate fallback behaviors in unstructured natural text, we follow Min et al. () and prompt the model to create biographies of entities from five popularity levels, analyzing the resulting facts.
We sample 25 entities from each popularity level.
- 3.
Qampari (Qamp): We sample 100 questions from the dataset introduced by Amouyal et al. (), which contains naturally occurring list questions with answers from Wikipedia.(^3^33While similar to our TriviaFacts dataset, this dataset is known to have non-exhaustive answer sets and different possible phrasings for the same answers, which can result in correct answers being flagged as hallucinations.)
- 4.
FakeQampari (FQamp): We replace the subjects from Qampari with made-up names, verifying they do not exist, forcing uncertainty.

Table 1 provides example questions. For additional details on the datasets, see Appendix A.

#### Generating predictions

To remove behaviors caused by different decoding schemes, we perform our analyses with greedy decoding unless stated otherwise (Section 5.1 investigates the effects of temperature sampling).
When requiring models to provide answers to the Triv data, we prompt the model to produce a list of up to 25 answers (see Table 1 for example), thus forcefully pushing the models to recall facts, even when there are none.
We ablate this behavior by removing the pre-defined length of the answer list and including specific demonstrations to complete the list while abstaining instead of generating incorrect facts (Section 5.2).

#### Evaluation metrics

Given a generated output, we first parse it into a set of facts, and evaluate each one independently as correct, repeated or hallucinated.
For the list questions datasets (Triv, Qamp and FQamp), this parsing is mostly straightforward as the generations are structured as lists and the ground truth is given as a set of answers.
For open-ended generation, we use FactScore Min et al. () to extract atomic facts and verify them against the entities’ Wikipedia entries.
As models frequently continue generating tokens after the completion of the instruction prompt, we further detect what the model did at that point.
Namely, we consider the following options: 1) generating EOS token, 2) changing the topic, for example, by creating a new list/biography which we refer to as topic change or 3) continuing to predict tokens indefinitely (until the token budget is exhausted) within the same sentence/paragraph of the answer which we note by bad format.
For additional information on the parsing process and example generations and endings, see Appendix B.

#### Models

We perform our experiments on a variety of model families, sizes, pretraining corpora sizes and finetuning stages.
We evaluate both the base and chat-specific checkpoints of Llama 2
which were finetuned on instruction and dialogue data, as well as on the recently introduced Llama 3 models Touvron et al. (); AI ().
Secondly, we use OLMo Groeneveld et al. (), which comes in multiple model sizes, includes intermediate checkpoints throughout the pretraining phase and also offers instruction-tuned variants.
Finally, we make use of the Pythia model family Biderman et al. () which comes in 8 different scales, each with 154 intermediate pretraining checkpoints.
We also include Dolly models which are instruction-tuned Pythia checkpoints (Conover et al., ).
This suite of models allows us to control for different factors, such as pretraining data, model scale and training procedure, examining the effect of each factor on the emergence of fallback behaviors.

## 4 Fallback Behaviors of Different LLMs

Scaling up models and training data improves performance and reduces undesired artifacts (Kaplan et al., ; Brown et al., ). Incorporating instruction-following phases aligns LLMs’ outputs with human preferences (Ouyang et al., ). In this section, we investigate how these improvements influence model behavior under uncertainty. We first focus on the trade-off between sequence repetitions and hallucinations, measured as discrete shifts in fallback behaviors. Due to the various forms and broad definition of degenerate text, measuring its appearance is not straightforward. We revisit this in depth in Section 6, demonstrating that the fallback shift is a continuous rather than discrete process.

### 4.1 Model scale dictates the fallback behavior

Figure: Figure 3: Larger models hallucinate instead of abstaining. When completing a list of facts about fictitious entities, larger Pythia models produce more hallucinations (orange), while smaller models repeat facts (blue). Models never abstain from providing incorrect facts.
Refer to caption: x3.png

To minimize confounding factors and understand the direct effect of model scale on fallback behaviors, we generate predictions with greedy decoding over our TriviaFacts data, and analyze the results by model family and scale.
Since Pythia models were all trained on the same data in the same manner, they are the most comparable.
Figure 2 shows that larger models recall more correct answers on average (green bar) and have lower failure rates in understanding task formats (red bar), as expected.
However, a clear trend emerges: while smaller models struggle to recall many facts and resort to repeating the same ones (blue bar), as the number of parameters increases, repetitions are replaced with hallucinations (orange bar).
This trend is consistent across the OLMo and Llama 2 model families (Figure 17).
We also tested naturally occurring list questions from our Qampari subset, confirming the same trends (Figure 18).

Finally, we test what happens when we push uncertainty to the limit using our FakeQampari dataset, which has no correct answers.
One might expect models to abstain, indicating they do not know the entity.
However, as Figure 3 shows, not only do the models fail to abstain, but we also observe that larger and more advanced models are more likely to fabricate facts compared to their smaller counterparts.
Specifically, the proportion of hallucinated answers more than doubles from approximately $15\%$ in models with fewer than one billion parameters to over $30\%$ in larger models, while the proportion of repetition decreases.

### 4.2 Models shift fallbacks during pretraining

Figure: Figure 4: Models that train longer shift to complex fallbacks. The more training tokens Pythia-6.9B checkpoints see (in billions), the more hallucinations they produce and fewer repetitions they generate. The green line marks the number of ground truth answers.
Refer to caption: x4.png

Most LLMs are trained with autoregressive language modeling objective, maximizing the log probability of the next token given some context.
Increasing the number of tokens seen during training lowers perplexity and improves language understanding (Kaplan et al., ). Using intermediate checkpoints from the OLMo and Pythia model families, we study fallback behavior during pretraining.
Figure 4 depicts the fallbacks breakdown of Pythia-6.9B across training, showing that initially, after seeing only
2-4 billion tokens, it mainly repeats facts (blue).
As training continues, it produces more correct answers (green) and more incorrect unique facts, i.e., hallucinations (orange), while repeating facts less. Similar trends were observed for Pythia-12B checkpoints, as well as for OLMo models (see Figures 19 and 20).

### 4.3 Instruction finetuning shifts behaviors

Recently, instruction finetuning (Ouyang et al., ) has been adopted as a valuable method to improve model performance and align its generation with human preferences.
Although one might assume such training reduces hallucinations, OpenAI et al. () suggest it increases model miscalibration, resulting in hallucinations associated with high logit values.

Repeating the experiments from Section 4.1 and comparing models to their instruction-tuned counterparts, we see a similar shift in fallback behavior; instruction-tuned models generate fewer repeated sequences and more hallucinations on average (Figures 21 and 22).
For the OLMo and Llama 2 family which had undergone more finetuning than Dolly checkpoints, the results are much more pronounced, with the hallucinations portion almost doubling between scales.
While pretrained LLMs are generally unable to generate sequences shorter than what they encountered during training, instruction-tuned models are more likely to produce an EOS token, preempting the generation early and resulting in fewer facts.
However, we also note that while instruction finetuning can improve alignment with human preferences, it can sometimes cause finetuned models to diverge more frequently, resulting in some bad format generations.

### 4.4 Similar trends in open-ended generation

Figure: Figure 5: Larger Pythia models hallucinate more when generating biographies. When producing biographies of rare entities, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x5.png

To mimic real-world user requests, we use the BioGeneration dataset, sampling completions from a subset of the Pythia model scales with a temperature of $\tau=0.5$.
We use FactScore (Min et al., ) to parse each generated biography into atomic facts and let ChatGPT 3.5 (Ouyang et al., ) verify them against Wikipedia entries.(^4^44While FactScore, which relies on ChatGPT 3.5, is not fully robust and can miss some atomic facts or incorrectly label them, we assume such errors occur at similar rates across generations, resulting in reliable trend analysis.)
Without the limitation on number of facts to produce, we observe that the larger models also generate more facts.
Interestingly, when averaging over the *very-rare* entities (Figure 5) we see that even in this natural scenario, when models are required to elaborate on topics they know little about, they fall back to the same behaviors, with shifts occurring as predictably as in the controlled settings.
Similar trends emerge over the rest of the popularity levels, though the more frequent an entity is, the more likely the models are to be able to recall facts for them and less uncertainty they face, thus making the results less useful for our analysis (Figures 27, 28, 29, 30 and 31).
This aligns only partially with Min et al. (), who found stronger models generate more atomic facts and struggle more with rare entities. However, they observed that within a model family, larger models are generally more precise (i.e., hallucinate less). In contrast, Lin et al. () found the largest models are the least truthful, which aligns with our findings.

## 5 Factors Influencing the Fallback Behaviors of an LLM

In this section, we shift our focus from comparing different models to understanding the factors influencing the fallback behaviors of a frozen prertained model used for inference.

### 5.1 Effect of sampling methods

Figure: Figure 6: Increasing the sampling temperature shifts fallback behavior. An average of 5 completions from Pythia-12B on TriviaFacts for varying temperatures. Green line marks the number of ground truth answers.
Refer to caption: x6.png

So far in our experiments, we mostly used greedy decoding to obtain responses from the models.
However, in real-world applications, decoding often includes sampling from the model’s output distribution and including repetition/frequency penalties (e.g., (Holtzman et al., ; Keskar et al., ; Kumar et al., )).
We analyze the effect of temperature sampling on fallback behaviors by repeating our experiment on TriviaFacts, while generating five different sequences per model for each input, with increasing levels of randomness (using larger values of temperature).
Figure 6 shows the results for Pythia-12B, revealing a surprising finding – while higher levels of randomness mitigate some repetitiveness and help models to break out of endless loops in the generation,(^5^55This is expected as even when copying previous tokens with high probability such as $0.99$, continuing the generation long enough will inevitably lead to generating a token that is not part of the loop, thus steering the text to a new direction.) it also causes a shift towards hallucinations.
Moreover, we observe that introducing randomness reduces the number of correct facts, which could be attributed to the random skipping of correct facts associated with low confidence in the model.

To assess whether our findings in Section 4.1 hold in realistic setups that involve random sampling, we repeat them with $\tau=0.5$. This choice of temperature sets a good balance between the model’s performance on the task and the amount of degenerate text it outputs (Figure 6).
Figure 23
confirms that even with additional randomness, all fallback behaviors emerge, with shifts occurring as predictably as in the greedy-decoding case.

### 5.2 Models “volunteer” hallucinations

In many previous experiments, we deliberately placed the model in high-uncertainty scenarios, prompting it to produce additional facts when none existed.
To lift this restriction, we modify the base prompt in the TriviaFacts data by removing the requirement for a specific number of answers (25).
For example, the modification to the example in the first row of Table 1 would result in the prompt ‘‘The following colors are in the Olympic rings:’’, allowing the model to stop generating additional answers once it exhausted its knowledge.
However, the same trends continue to emerge (Figure 24), showcasing that even in natural situations without synthetically induced uncertainty, LLMs still fall back on the same behaviors and fail to use more appropriate “exit strategies”, such as topic changes or explicit abstaining.

### 5.3 Can fallbacks be prevented by prompting?

Kadavath et al. () find encouraging evidence that LLMs may be calibrated (i.e. their confidence approximates the true probability of the output) and able to assess what they don’t know, especially when they are larger.
However, Kapoor et al. () recently showed that this observation does not hold for some popular open-source models, and Yona et al. () showed that LLMs struggle to express their uncertainty in words.
We aim to understand whether LLMs are internally aware of their uncertainty,
and if this awareness can reduce unwanted behaviors.

We modify the prompts as follows: For instruction-tuned models, we add the following prefix to each prompt
‘‘Complete the following list with facts you are sure of, and stop when you cannot recall additional facts’’.
For pretrained models, we add a prefix with three demonstrations for in-context learning, each consists
of an easy list question and its corresponding answers followed by a topic-change to a new list
(Figure 25 gives the full prefix).
While for Pythia and OLMo we observe a minor increase in abstaining behavior at the expense of hallucinations, the overall trend remains the same, with hallucinated facts emerging abundantly (Figure 26).
In comparison, both Llama 2 and more pronouncedly Llama 3 manage to abstain by changing topics often, but the portion of hallucinated facts still increases with model size.
We conclude that fallback behaviors are inherent to current pretrained LLMs and emerge “unintentionally” and unavoidably under uncertainty.

## 6 Fallback Behaviors in One Generation

While we established that both model strength and the decoding method impact the fallback behaviors of a model, these parameters are decided ahead of time.
In this section, we focus on a single model at a time and investigate the effect of generation length on emergence of fallback behaviors.

### 6.1 Emergence of fallbacks during generation

Figure: Figure 7: Models shift fallback behavior during a single completion. Order of fallbacks per generation for a Pythia-12B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x7.png

We look at the facts generated by the model in response to a given query as an ordered list of labels (i.e. *correct*, *hallucination*, *repetition*).
For example, Figure 7 depicts in each row the 25 labels of the facts produced by Pythia-12B for each of the 95 samples in TriviaFacts.
Surprisingly, we observe that almost without exception, the model first generates all the correct facts it can (green), then shifts to producing hallucinations (orange), and finally shifts further to repeating previous facts (blue).
This is especially noteworthy as we see that the fallback behavior is not determined by the question,
and the ordering between fallbacks follows the same ordering as when increasing model strength.
This trend holds regardless of the model, the dataset, or the decoding method (see Figures 32, 33, 34, 35, 36, 37, 38 and 39 for further results).

To quantify this phenomenon, we denote a label by 1 for repetition, 2 for hallucination, and 3 for correct, and define

$$ $\texttt{ShiftScore}({f_{1},\dots,f_{n}})\coloneqq\frac{1}{n-1}\sum_{i=1}^{n-1} {\mathbbm{1}_{f_{i+1}\geq f_{i}}}$ $$

Where $f_{1},\dots,f_{n}$ is an ordered list of fact-labels ($f_{i}\in\{1,2,3\}$), and $\mathbbm{1}$ is the indicator function.
For each pair of dataset and model, we measure the ShiftScore for each generation.
We then run a Mann-Whitney U-test (McKnight and Najab, ) to compare the results to the expected ShiftScore if the list of facts was produced in a random order (more details in Section D.2).
We find that for all tested pairs, the p-value is $<10^{-8}$ (see Table 2 for full analysis), and the U-statistic is always positive.
We thus conclude that the ordering is not random, and follows the aforementioned hierarchy of fallback behaviors.

When investigating the fallback behaviors in a single generation, one may expect to observe occurrences of verbatim recollection of training samples, as suggested by Nasr et al. (). To address this, we analyzed the generations from our experiments both qualitatively and quantitatively, and found no evidence of such behavior. We therefore hypothesize that verbatim generation of training samples stems from other factors than uncertainty regarding factual information, and is more prevalent in the presence of out-of-distribution inputs.
For further details, see Appendix C.

### 6.2 Fallback shifts is a continuous process

Figure: Figure 8: Model output gradually degenerates with increased generation length. When generating biographies for rare entities, model output degenerate as completion length increases, compared to the human baseline (Wikipedia entries for the same entities). Stronger models’ output deteriorates more slowly.
Refer to caption: x8.png

When analyzing generations as a discrete list of facts, we also find discrete shifts in fallback behaviors as the model’s uncertainty grows.
While examining strict repetition of facts is of particular interest, one may also consider a softer measure of degenerate text generation.
For example, producing a list of URLs such as *https://page.domain/xyz*, *https://page.domain/xyq*, *https://page.domain/wyz* is not strictly a repetition nor necessarily a hallucination, but it is clear that some form of conditional repetitiveness is occurring.
Similarly, producing the same entities in random permutations is clearly undesired behavior and is not strictly a repeated sequence.
For example, in Figure 12 we can see that the model starts repeating previous facts such as the shows *Elsa Pataky* appeared in, though with a slightly different phrasing, before it begins repeating previous sequences verbatim.

To measure such phenomena, we introduce a DiversityScore.
Given a sequence of predicted tokens $x=t_{1},\dots,t_{n}$ from the model’s vocabulary $\mathcal{V}$, we define $\texttt{DiversityScore}(x)\coloneqq\frac{1}{n}\sum_{v\in\mathcal{V}}{\mathbbm{
1}_{v\in x}}$, i.e. the number of unique tokens in the sequence divided by the sequence length.
When $n\ll|\mathcal{V}|$ we expect $\texttt{DiversityScore}\approx 1$ while a repetitive sequence will have a DiversityScore that diminishes rapidly towards 0.

When experimenting with naturally occurring (human-generated) sequences, such as Wikipedia paragraphs, for sequences up to $512$ tokens, the DiversityScore is almost always higher than $0.6$ and often ranges around $0.7-0.8$ even for longer sequences (see Figure 8).
Specifically, we use the subset of BioGeneration entities that are very frequent according to Min et al. (), as we expect the least amount of uncertainty to appear and the most full and diverse generations, as discussed in Section 4.4.
We compare the Wikipedia paragraphs for these entities to models’ outputs when generating biographies for them.
For all models’ outputs, the DiversityScore diminishes much faster, with weaker models crossing the $0.5$ threshold at around 150 tokens (i.e., after only 150 tokens, every other token is repeating a previous one).
We include the same analysis on generations from the stronger model Llama 3 8B (AI, ) for comparison.
Indeed, while it produces slightly more diverse outputs, diversity diminishes towards zero as the sequence length grows.

We conclude that the shift in fallback behaviors towards the degenerate end does not occur in a discrete phase shift, but is a continuous process that exacerbates as the generation length grows.

## 7 Related Work

#### Repetitions and degenerate text

Holtzman et al. () first attributed the tendency of LMs to generate highly repetitive and degenerate text to greedy sampling and suggested nucleus sampling as a possible mitigation. Follow-up work demonstrated the role of uncertainty in generating degenerate text and proposes various solutions using random, controlled, or constrained generation techniques Keskar et al. (); Kumar et al. (); Zhang et al. (); Finlayson et al. (); Su et al. (). In a parallel approach, Olsson et al. () focused on understanding the intrinsic mechanisms that cause models to copy previous inputs.

#### Hallucinations

As random sampling techniques became ubiquitous and LLMs grew in size and capability, the main focus shifted toward understanding the causes and proposing solutions to the generation of non-factual text, commonly referred to as hallucinations. Recent work has focused on understanding how and why hallucinations emerge during generation Rashkin et al. (); Zhang et al. (); Adlakha et al. (); Kim et al. (), reducing hallucinations (e.g., by retrieval-augmentation or prompting) Roller et al. (); Shuster et al. (); Dhuliawala et al. (), and detecting hallucinations Zhou et al. (); Liu et al. (); Honovich et al. (); Min et al. (); Mishra et al. (); Gottesman and Geva (). For a survey on hallucinations in natural language generation, see Ji et al. ().
In concurrent work, Denison et al. (, inter alia) demonstrated how models generalize during training and learn to shift from simple dishonest strategies such as sycophancy to generating false facts with premeditation. Similarly, Band et al. () suggest that the problem of hallucinating rather than abstaining is an issue with the calibration of the model, as was also hypothesized by OpenAI et al. ().

#### Uncertainty in language modeling

The phenomenon where LMs abstain or hallucinate when uncertain is well-known and has been reported in previous work Xiao and Wang (); Lin et al. (); Snyder et al. (); Baan et al. (); Kang et al. (). Recent studies have examined whether the generation probability of LMs is calibrated Kadavath et al. () and whether they can express their uncertainty in natural language Li et al. (); Yona et al. (). Unlike previous research, our focus is on understanding the varying factors impacting the different fallback behaviors of LMs under uncertainty.

## 8 Conclusion

This work links the notorious unwanted behaviors of LLMs, such as degenerate and repetitive text and hallucinations, showing that they are all fallback behaviors for models when faced with uncertainty.
We provide abundant evidence that these behaviors emerge with a clear ordering between their appearances, either when comparing similar models of different strength, different decoding strategies and even in a single generation.
Our work suggests that these fallback behaviors are inherent to current LLMs and that the ubiquitous methods to alleviate such issues may simply replace one fallback by another.
Moreover, increasing model strength by training longer or adding more parameters enhances overall performance and shifts its fallbacks towards more complex range.
However, as generation length grows, even the strongest models will resort to hallucinations and may eventually produce degenerate text.

## Limitations

In this work we study different fallback behaviors of language models when faced with uncertainty.
While we conduct multiple experiments trying to mimic real-world usage of such models as much as possible, there are several confounders that may still differentiate our controlled experiments than behavior in the wild.
First, many of the commodity products are wrapped in additional levels of verification layers to reduce the effect of such behaviors, and it is possible that when observed as a whole, the behaviors of these products could differ significantly than their underlying language models.

Second, while we study the effect of random sampling, it becomes more common to apply even stricter modifiers to the language model next-word prediction distribution by using methods such as nucleus sampling, top-k decoding, repetition penalty and more (Holtzman et al., ; Keskar et al., ; Finlayson et al., ).
We leave to future work to examine such decoding strategies on fallback behaviors.

Third, we study the effect of instruction-following finetuning on fallback behaviors, but it remains possible that additional preference alignment may allow models to be instructed not to hallucinate.
While recent work (Kapoor et al., ; Yona et al., ) has showed these models are not well calibrated and cannot tell whether they hallucinate or not, it is an open research and it is possible that eventually it will be a viable solution.

Finally, this work focuses on producing factually correct facts in natural language, and it remains for future work to investigate similar phenomena in producing faithful text to in-context information (such as entities position and attributes with a story), as well as when the task involves synthetic language such as when producing code.

## Acknowledgments

This research was partially supported by The Yandex Initiative for Machine Learning,the Israeli council of higher education, the Len Blavatnik and the Blavatnik Family foundation, and the European Research Council (ERC) under the European Union Horizons 2020 research and innovation programme (grant ERC DELPHI 802800).
This work was completed in partial fulfillment for the Ph.D. degree of the first author.

## Appendix A Datasets

As discussed in Section 3, we make use of multiple datasets for our experiments. In this section, we provide further details on the construction of each of them. We release all the datasets used in our experiments at [https://github.com/Mivg/fallbacks](https://github.com/Mivg/fallbacks).

### A.1 TriviaFacts

When creating this dataset, we set the following desiderata:

- 1.
Exhaustiveness: In order to label all correct answers as such, we verify that our ground-truth answer set is exhaustive.
- 2.
Non-ambiguity: To avoid incorrectly labeling a correct answer as a hallucination, we avoid questions where the answers may be phrased in many ways and focus on short answers with a single common way to refer to them.
- 3.
Easiness: To be sure models are able to recall correct facts, we choose only questions where the answer list contains at least some answers that any graduate student can produce, as a proxy to the knowledge contained in language models that are trained on web data and Wikipedia.
- 4.
Diversity: To avoid biases in evaluations, we set out to create a diverse set of questions that relates to as many domains as possible, spanning science, sports, culture, politics, geography, and more.
- 5.
Uncertainty: To ensure questions induce uncertainty, we restrict the size of the ground-truth answer set to 10. As we ask models to produce 25 answers, we thus ensure they will become uncertain even when recalling all the correct answers.

We collect a set of nearly 300 candidate questions through a mix of manual suggestions and interactions with ChatGPT.
We then manually annotate each of the questions according to the desiderata above, verifying the correctness and completeness of the answer set.
After aggressive filtering, we are left with 95 high-quality questions that meet all of our requirements.

Figure: Figure 9: Larger Pythia models’ fallback behaviors on TriviaFacts when adding a colon at the end of a questions. Models with larger parameters count produce more correct facts (green) and hallucinations (orange) while less repeating facts (blue). The green line indicates the number of ground truth answers.
Refer to caption: x9.png

Table 1 shows the format of questions in TriviaFacts. In contrast to the other list questions datasets (Qampari and FakeQampari), here the prompts do not end with a “:”. To verify that this has no significant effect on the results, we append a colon to each question and repeat the experiments from Section 4.1. As depicted in Figure 9, the same trend repeat here, and the results are very similar to those in Figure 2.

### A.2 BioGeneration

Min et al. () introduce FactScore, which uses an LLM (e.g., ChatGPT) to parse an unstructured passage into atomic facts and verify them independently against a knowledge base.
Min et al. () use topics from Wikipedia and divide them into five popularity levels based on their frequency in the knowledge base, from very rare to very frequent. They then require a model to generate an open-ended biography for each topic (entity) and use the respective Wikipedia page as the ground-truth knowledge source to verify facts. We randomly sample 25 topics from each of the five popularity levels and use them throughout our experiments.

### A.3 Qampari

We use the dataset as introduced by Amouyal et al. (). We filter the questions to those where the answer is a set of size $\geq 3$ and sample 100 random questions. Then, we rephrase each question to be in the format depicted in Table 1. Where appropriate, some of the questions follow a slightly different template. For example, one of the questions is *“Larry Cohen wrote and directed the following 25 works of art:”*.

### A.4 FakeQampari

We take the questions from Qampari (see Section A.3) and employ ChatGPT to replace the subject entity in each question with an alternative entity name which sounds plausible but does not exist (for example, choosing a Japanese name for an anime creator). We manually verified the generated questions, while checking that all new subjects feel plausible in the context and that they refer to entities that do not exist.

## Appendix B Parsing Answers

In this section, we provide further implementation details on the process used to parse the answers from each generation.
We make our code and the model generations available in [https://github.com/Mivg/fallbacks](https://github.com/Mivg/fallbacks).

### B.1 List Questions

#### Extracting answers

In Table 1, we show examples of inputs given to the model for completion.
To steer models to complete the answers in a specified format, we append to each question the suffix ‘‘\n 1.’’. In almost all cases, the models indeed generate completions in the format of ‘‘\n 1. <answer 1>\n 2. <answer 2> …’’, which allows us to easily extract the answers (Figure 10).
In some cases, the generations include no \n or extra newline characters, but those cases are easily dealt with by a simple regex.
The next most common case is a given answer as a comma-separated list.
We identify those cases when none of the above options were detected and at least five commas appeared in the first sentence of the generation (no newlines or periods).
Finally, if none of the above was detected but newline characters are identified (one or more) between short lines, we treat each new non-empty line as an answer.
If none of the cases were identified, or a detected structured format is violated without new line characters, we collect the answers until that point and mark the difference from the expected 25 answers as missing due to Bad Format.
All answers are normalized by removing extra white spaces to evaluate for repetitions.

#### Identifying end of lists

Since pretrained language models are often trained with sequences of a predetermined length, they do not produce end-of-sequence (EOS) tokens and continue to generate tokens until their budget is exhausted.
As such, to avoid false-positive hallucination detection, we have to be able to identify when the model stopped producing answers for the given prompt and started generating completions for other topics.
The easiest to identify and most common case is when the models generate the completion in the structured format as described above.
In such cases, we can simply identify when the structure is violated (e.g., after the 10th answer the model stops enumerating answers and changes the topic), or take the first 25 answers given.
Another common pattern used by many models is a Topic change by including the prefix “The following …” (which we use as the prefix in our prompts), followed by a new list of items.
Finally, some models (especially instruction-tuned ones) explicitly output an EOS token to mark the end of the generation, in which case we mark the missing answers to complete the expected 25 as missing due to EOS.

#### Evaluating correctness

To avoid labeling answers as hallucinations when they are correct but phrased differently, we perform a relaxed evaluation for correct answers.
Namely, each given answer is normalized by removing redundant white spaces, articles, and punctuation, removing any parentheses, and making all characters lowercase.
When multiple synonyms for the correct name exist (such as *Southern Ocean*, which is another name for *Antarctic Ocean*), we attempt to include both as possible correct answers.
We then consider a produced answer correct if it has an $f_{1}$ score of at least $0.55$ with an answer in the ground-truth set.

We verify our methods manually by sampling generations from multiple models and experiments and find that the extraction, end-of-generation identification, and evaluation are correct in over $95\%$ of the cases.

### B.2 Biographies Generations

As we rely on FactScore (Min et al., ) to extract atomic facts and evaluate them, our task here is mainly identifying topic changes to avoid false detection of hallucinations.
To do so, we look for a prefix indicating a new biography is being generated, for special keywords such as *References*, *Discography*, etc., if multiple newline characters appear, or if a title-like line is encountered.
We also consider any new line that starts with the subject name or a pronoun as a continuation of the biography.

After extracting only the part of the generation we consider as the biography and indicating the strategy to change the topic, we split the biography into sentences and delegate to FactScore (using ChatGPT 3.5 (Brown et al., ; Ouyang et al., )) to extract atomic facts and validate them against the entity’s Wikipedia page.
An example of a full generation is given in Figure 12 and the list of extracted atomic facts and their labeling is given in Figure 13.

Figure: Figure 10: An example of a generation where the output followed the structured format precisely. For the extracted and labeled answer set, refer to Figure 11.

Figure: Figure 11: Figure 10 shows an example of a generation where the output followed the structured format precisely. We display the extracted set of answers and their labeling according to our evaluation. An example breakdown of a generated biography into atomic facts and their labels is provided. Correct facts are noted by (C), hallucinated ones by (H), and repeated ones as (R).

Figure: Figure 12: An example generated biography by Llama 3 8B, illustrating the different fallback behaviors of LLMs, gradually shifting from factually correct claims, through hallucinations and degenerate text, to sequence repetitions. The breakdown into atomic facts and their labels can be found in Figure 13
Refer to caption: x10.png

Figure: Figure 13: An example breakdown of a generated biography into atomic facts and their labels. Correct facts are noted by (C), hallucinated ones by (H), and repeated ones as (R). (EOS) marks that the generation was preempted by the model, with no additional unrelated content generated. For the original generation as produced by the model, see Figure 12.

## Appendix C How Models Change Topics

Throughout our experiments, we analyze the classification of atomic facts in models’ generations, up until a point where they change the topic.
Nasr et al. (); Geiping et al. () demonstrate how extremely out-of-distribution inputs can cause models to fall back into recalling training samples verbatim, and Haviv et al. (); Stoehr et al. () showed that generation of memorized sequences has a distinct internal “profile” from generation of non-memorized sequences.
In contrast, Prashanth et al. (, inter alia) investigate memorization and find that it can be caused by different factors such as duplicated training data, repeating common and predictable patterns, and the recollection of sequences that are neither.

Inspired by this line of work, we analyzed how models change topics, and in particular, if memorized sequences emerge in these cases.
We manually inspect such generations from multiple models, for both our TriviaFacts and BioGeneration datasets, using both greedy and temperature sampling.
Using Infini-gram (Liu et al., ),
we iterate over sliding windows of lengths 8 and 16 tokens to look for possible occurrences of verbatim recollection, which we manually inspect.
We found no cases where a sequence longer than a few tokens repeats verbatim, and even then it comprises of common patterns, such as ‘‘public static void main’’.

Upon manual inspection, we have found that in almost all cases, the “topic change” included a repetition of the exact same content as before, additional data in some template format (e.g. listing references for BioGeneration samples), or another generation in the same format as the question (e.g. another list about another topic for TriviaFacts and another biography for a different entity for BioGeneration).
The only notable behavior observed was models attempting to provide some code snippets to support the answer-list generated, as seen in Figure 14.

Overall, we did not observe any evidence of verbatim generation of training samples in the models’ generations. We therefore hypothesize that this behavior stems from other factors than uncertainty regarding factual information, and is more prevalent in the presence of out-of-distribution inputs.

Figure: Figure 14: A few examples of generation with greedy decoding. $<\cdot>$ indicate the generating model, and \n indicate line breaks in the generation. We use ‘‘…’’ when the sequence becomes repetitive.

## Appendix D Additional Results

In this section, we provide additional results to support the results discussed in Sections 4, 5 and 6.
As discussed in Section 3, we evaluate three model families over multiple datasets and in various settings.
For the sake of brevity, we include here a representative set of results and share the code and results to produce all plots for the rest of the experiments.

Figures 17 and 18 depict the scaling behavior of larger models on the TriviaFacts and Qampari datasets respectively.
Figures 19 and 20 shows fallback trends during pretraining for Pythia-6.9B and 12B models in the former and OLMo-1B and 7B models in the latter.
Figures 21 and 22 gives similar results when adding instruction finetuning to models on the TriviaFacts and Qampari datasets.
In Figure 25 we ablate the prompt for instruction finetuned models to nudge them into abstaining rather than producing hallucinations, and Figure 26 uses in-context examples to do the same for pretrained models.
Finally, Figures 27, 28, 29, 30 and 31 bring a breakdown of the results on the BioGeneration datasets with different levels of popularity.

### D.1 Exclusion of Llama 2 13b Checkpoint

We find that Llama 2 13b as released in [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) almost always produces extremely poor results. Upon manual inspection of its outputs, we conclude that there is likely a bug in the uploaded weights of this specific model, and thus we exclude it. We provide a few examples in Figure 15.
We note that for the other checkpoints, as well as the chat variants of all three sizes, the generation are considerably of higher quality, further supporting this decision.

Figure: Figure 15: A few examples of generation with greedy decoding from the meta-llama/Llama-2-13b-hf checkpoint. Bold text indicates the prompt. Boldface text in $\mathbf{<>}$ indicates authors’ notes. This model often produces incoherent text, leading us to conclude there is a bug in the uploaded weights.

Figure: Figure 16: Prefix given to pretrained language model before each sample of the TriviaFacts dataset to encourage in-context learning and demonstrate how topic-change can be done, half-way through a list, when the full answer set is exhausted, for the experiments mentioned in Section 5.2

### D.2 Order of Facts in a Single Generation

**Table 2: The results (p-values) of running Mann-Whitney U-test on the ordering of facts with respect to the predicted hierarchy introduced in this paper, for each model-dataset pair. In all cases, the p-value is less than $10^{-8}$ while the U-statistic is strictly positive suggesting the order of facts is far from being random.**
| Model | Qampari | Triv | Triv ($\tau=0.5$) |
| --- | --- | --- | --- |
| Pythia-70M | $1.7\times 10^{-12}$ | $2.2\times 10^{-13}$ | $7.8\times 10^{-11}$ |
| Pythia-160M | $1.4\times 10^{-18}$ | $2.0\times 10^{-12}$ | $2.3\times 10^{-15}$ |
| Pythia-410M | $4.8\times 10^{-22}$ | $3.2\times 10^{-20}$ | $1.6\times 10^{-12}$ |
| Pythia-1B | $1.3\times 10^{-18}$ | $7.8\times 10^{-23}$ | $1.5\times 10^{-16}$ |
| Pythia-1.4B | $1.5\times 10^{-17}$ | $1.0\times 10^{-25}$ | $1.4\times 10^{-17}$ |
| Pythia-2.8B | $8.4\times 10^{-16}$ | $2.1\times 10^{-22}$ | $1.7\times 10^{-19}$ |
| Pythia-6.9B | $3.3\times 10^{-14}$ | $3.8\times 10^{-28}$ | $1.1\times 10^{-17}$ |
| Pythia-12B | $8.0\times 10^{-19}$ | $9.8\times 10^{-28}$ | $1.1\times 10^{-22}$ |
| OLMo-1B | $4.9\times 10^{-15}$ | $8.3\times 10^{-22}$ | $2.4\times 10^{-21}$ |
| OLMo-7B | $7.4\times 10^{-20}$ | $3.8\times 10^{-21}$ | $9.2\times 10^{-24}$ |
| Llama2-7B | $8.7\times 10^{-11}$ | $9.2\times 10^{-26}$ | $6.2\times 10^{-18}$ |
| Llama2-70B | $5.1\times 10^{-09}$ | $4.3\times 10^{-18}$ | $1.4\times 10^{-12}$ |
| Llama3-8B | $2.2\times 10^{-13}$ | $2.1\times 10^{-11}$ | $1.4\times 10^{-12}$ |
| Llama3-70B | $8.2\times 10^{-10}$ | $3.6\times 10^{-11}$ | $7.8\times 10^{-11}$ |

Section 6.1 introduces the ShiftScore, which measures how predictable the order of facts is with respect to the hierarchy between fallback behaviors as introduced in this work.
To perform the Mann-Whitney U-test, we consider only answer sets with at least five unique answers and model-dataset pairs with at least 30 such answer sets.
For each such set, we compute the expected ShiftScore of a random ordering of the answers by taking 1000 random permutations of their order and averaging their ShiftScore.(^6^66For TriviaFacts with temperature sampling, we use the ordering of the first of the five generations sampled for each question.)
We then perform the statistical test on the list of ShiftScore values from the original ordering of the model against the scores of the random orders.
Table 2 shows the p-value of the two lists of scores coming from the same distribution.
In all cases, the U-statistic was strictly positive, allowing us to conclude that the original ordering follows the expected order in a statistically significant way.

Additionally, in Section 6.1 we discuss the ordering of fact-labels within a single generation, as presented in Figure 7 for Pythia-12B model on TriviaFacts.
Figures 32, 33, 34, 35, 36, 37, 38, 39, 40 and 41 give similar plots for other models.

Figure: Figure 17: Larger models resort to more sophisticated fallback behaviors on the TriviaFacts dataset. Here, models of increasing size produce more correct facts (green) and hallucinations (orange) while producing fewer repeated facts (blue). The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x11.png

Figure: Figure 18: Larger models resort to more sophisticated fallback behaviors on the Qampari dataset. Here, models of increasing size produce more correct facts (green) and hallucinations (orange) while producing fewer repeated facts (blue). The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x12.png

Figure: Figure 19: Pythia models that train longer shift to complex fallbacks. The more training tokens Pythia models see (in billions), the more hallucinations they produce and the fewer repetitions they generate (on TriviaFacts). The left group depicts the trend for Pythia-6.9B pretraining while the right group is for Pythia-12B. The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x13.png

Figure: Figure 20: OLMo models that train longer shift to complex fallbacks. The more training tokens Pythia models see (in billions), the more hallucinations they produce and the fewer repetitions they generate (on TriviaFacts). The left group depicts the trend for OLMo-1B pretraining while the right group is for OLMo-7B. The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x14.png

Figure: Figure 21: Instruction-tuned models resort to more complex fallback behaviors, on the TriviaFacts dataset. The Dolly models (instruction-tuned variants of Pythia) hallucinate more and repeat facts less, while also breaking out of loops more often and abstaining from producing additional facts (increase in red and pink bars). For the OLMo and Llama 2 families, which had a more robust phase of instruction-following training, the results are much more pronounced. The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x15.png

Figure: Figure 22: Instruction-tuned models resort to more complex fallback behaviors, on the Qampari dataset. The Dolly models (instruction-tuned variants of Pythia) hallucinate more and repeat facts less, while also breaking out of loops more often and abstaining from producing additional facts (increase in red and pink bars). For the OLMo and Llama 2 families, which had a more robust phase of instruction-following training, the results are much more pronounced.
Refer to caption: x16.png

Figure: Figure 23: Larger models use more sophisticated fallbacks even with random decoding. Results of different models on the TriviaFacts dataset with temperature sampling, setting the sampling temperature ($\tau$) to 0.5. Each completion was sampled five times. The horizontal green line indicates the maximum number of correct answers possible. The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x17.png

Figure: Figure 24: Larger models volunteer hallucinations, even when not asked for additional facts. When given prompts from TriviaFacts without specifying the number of items in advance (see Section 5.2), larger models continue to produce more hallucinations than their smaller counterparts and barely exhibit abstaining strategies (e.g., changing the topic). The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x18.png

Figure: Figure 25: Instruction tuned models continue to hallucinate, even when told to prefer abstaining over non-factual responses. When given the prefix ‘‘Complete the following list with facts you are sure of, and stop when you cannot recall additional facts’’ to encourage abstaining instead of hallucinating, all instruction tuned models fail to utilize internal uncertainty estimations if exists and continue to hallucinate. The models are only slightly more inclined to abstain (by changing topics or producing EOS tokens). The results are given for the TriviaFacts dataset, with “(IDK)” marking the model with the modified prompt. The horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x19.png

Figure: Figure 26: Larger models hallucinate more, even when shown how to abstain. When given the prefix from Figure 16 to encourage abstaining instead of hallucinating, larger Pythia and OLMo models continue to exhibit the same fallback behavior trends, with repetitions decreasing and hallucinations increasing as model size grows. The models are only slightly more inclined to abstain (by changing topics). Notably, while Llama 2 and Llama 3 models exhibit the predicted trends in the proportion of hallucinations, they are considerably more capable of abstaining when given the opportunity. Interestingly, Pythia-410M is able to change topics remarkably well, though when manually inspecting its outputs, we find that in almost all cases it produces a list of five facts, often with repetitions, and then continues to repeat this list indefinitely. The results are given for the TriviaFacts dataset, and the horizontal green line indicates the maximum number of correct answers possible.
Refer to caption: x20.png

Figure: Figure 27: Larger Pythia models hallucinate more when generating open-ended biographies on very rare entities. When producing biographies of entities with very low frequency of appearance in Wikipedia, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x21.png

Figure: Figure 28: Larger Pythia models hallucinate more when generating open-ended biographies on rare entities. When producing biographies of entities with low frequency of appearance in Wikipedia, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x22.png

Figure: Figure 29: Larger Pythia models hallucinate more when generating open-ended biographies on medium-popularity entities. When producing biographies of entities with medium frequency of appearance in Wikipedia, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x23.png

Figure: Figure 30: Larger Pythia models hallucinate more when generating open-ended biographies on popular entities. When producing biographies of entities with high frequency of appearance in Wikipedia, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x24.png

Figure: Figure 31: Larger Pythia models hallucinate more when generating open-ended biographies on very popular entities. When producing biographies of entities with very high frequency of appearance in Wikipedia, larger models generate more atomic facts, with an increasing rate of hallucinations.
Refer to caption: x25.png

Figure: Figure 32: Pyhtia-1.4B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Pythia-1.4B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x26.png

Figure: Figure 33: Pyhtia-2.8B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Pythia-2.8B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x27.png

Figure: Figure 34: Pyhtia-6.9B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Pythia-6.9B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x28.png

Figure: Figure 35: Pyhtia-12B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Pythia-12B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x29.png

Figure: Figure 36: OLMo-1B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a OLMo-1B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x30.png

Figure: Figure 37: OLMo-7B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a OLMo-7B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x31.png

Figure: Figure 38: Llama 2 7B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Llama 2 7B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x32.png

Figure: Figure 39: Llama 2 70B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Llama 2 70B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x33.png

Figure: Figure 40: Llama 3 8B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Llama 3 8B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x34.png

Figure: Figure 41: Llama 3 70B model shift fallback behavior during a single completion. Order of fallbacks per generation on TriviaFacts for a Llama 3 70B model—each row represents a specific prompt with the 25 produced facts. Green marks correct answers, orange hallucinations and blue repeated facts. Purple sequences indicate a topic change, and red ones indicate a divergence to bad-format. Questions are sorted by the number of consecutive repetitions.
Refer to caption: x35.png

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 5
presentation: 4
questions: I don’t understand what is meant in the sentence “Moreover, introducing randomness reduces the number of correct facts, which could be attributed to the random skipping of correct facts that have low confidence in the model.” Could you clarify?
soundness: 3
strengths: * The work offers a thorough analysis across various LLM families of different types of frequently-observed degenerate behavior
* The experimental setup seems like a reasonable way to control for epistemic uncertainty
* There are interesting empirical findings about the prevalence and characteristics of degenerate outputs from modern LLMs
summary: This paper looks at “fallback” behaviors in large language models in the face of epistemic uncertainty, specifically focusing on repetitive text, degenerate phrasing, and hallucinations. They set up scenarios to control the presence of epistemic uncertainty and characterise the outputs of various LLMs, evaluating how the number of parameters, amount of pretraining data, instruction-following training, and decoding impact the type of fallback behavior exhibited. They observe what they call a “fallback hierarchy” where as models become more uncertain (or if they are weaker to begin with), they rely more on basic degenerate behaviors. They posit that fallback behaviors are interconnected, based on the observation that models transition from one behavior to another as uncertainty decreases. Importantly, they find that larger, more sophisticated models also exhibit fallback behaviors in the face of uncertainty, even if they may be more sophisticated fallback behaviors and that some techniques claimed to mitigate degenerate behaviors have other adverse effects or only a limited ability to help
weaknesses: * The contributions of the work are limited: 
    * empirical observations are made but no insights about what the source of these behaviors are or how they can be fixed are given
    * The analysis is limited to the presence of epistemic uncertainty, but there are other scenarios in which degenerate behaviors occur (e.g. when adversarial prompts are given or in the face of aleatoric uncertainty) so we’re still far from a comprehensive empirical analysis of degenerate behaviors 
* The setups are rather contrived and since the work's results are only empirical, its not clear what the practical implications of them are
* While the authors claim to explore the impact of decoding on these fallback behaviors, they seem to only look at greedy decoding and temperature sampling. They also seem to only explore one prompt as a “mitigation” technique
* Some claims don’t feel adequately supported. E.g., “fallback behaviors… emerge…unavoidably under uncertainty” Even though a comprehensive set of prevention techniques weren’t explored

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
questions: See weaknesses
soundness: 3
strengths: The paper analyzes fallback mechanisms when the model encounters uncertainty—an important and timely issue. The experimentation is extensive, with tests on multiple models across various datasets to validate its conclusions. The findings are interesting and hold significant value for other researchers working on LLM hallucinations. Some of the conclusions seem less surprising (although empirical verification of them is still useful)

While the paper has notable strengths, there are some concerns (see weaknesses). I am open to raising my score once these issues have been addressed.
summary: The paper studies how LLMs behave in the face of epistemic uncertainty, claiming that the LLMs “fall back” to various strategies, such as hallucinations, degenerate text, and more when this occurs. The study explores these fallback strategies across several model parameters, including model size, pretraining tokens, instruction tuning, and decoding algorithms.

The paper presents some interesting findings such as:

1. Larger models tend to be more factual but are more likely to hallucinate as a fallback strategy rather than generate repetitive or poorly formatted text.
2. As the number of pretraining tokens increases, the number of factual responses also increases, though the model may hallucinate more frequently.
3. Instruction tuning makes the model's fallback strategies more complex without significantly changing the percentage of factual answers.
4. Increasing the sampling temperature slightly reduces factuality but decreases repetitiveness and occasionally enhances creativity.


The authors conduct experiments on several different models, checkpoints, and datasets.
weaknesses: 1. **Presentation Improvements**

   - The paper’s presentation could be refined. In its current form, it may take considerable time for readers to extract the main takeaways.
   - There are multiple task settings, and results can sometimes be difficult to interpret due to the variety of datasets used for each claim. Standardizing these settings could improve clarity.
   - In some cases, important results are presented only in the appendix, which can be frustrating for readers. Additionally, the graph captions could be more informative (for instance, specifying the dataset used for Figures 3 and 4).
   - Terminology is sometimes inconsistent—for example, the term "stronger models" is used, but it’s unclear what this means.

2. **Experimentation Concerns**

   - Why are instruction-tuned models tested only on TriviaFacts? Although list-based questions may simplify evaluation, this limits conclusions to a single, small, self-created task (hence weakening the claim) 
   - Some parameters are analyzed independently, even though they often co-occur. For example, model size could be varied specifically within instruction-tuned models. This approach sometimes leads to inconsistencies. In Figure 21, llama-3-8B-inst is shown to be more factual (% factual) and hallucinates significantly more than llama-3-70B-inst—contradicting the earlier finding that larger models tend to produce more factual answers while hallucinating more. This also appears inconsistent with broader findings, such as in the Llama-3 report, where llama-3-70B is generally noted as more accurate.
   
   These outliers and inconsistencies warrant further discussion.

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confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: •	If this paper focuses on “epistemic uncertainty” and do not explore other types of uncertainty, why this paper is titles “under epistemic uncertainty”.
•	Is the TRIVIAFACTS dataset evaluated? The question mentioned in the paper, which is “The following 25 colors are in the Olympic rings.” seems to mislead LLMs to repeat and hallucinate.
soundness: 2
strengths: •	This paper provides a controlled analysis of LLM undesired behaviors, showing that strengthening models increases hallucinations over degenerate text and repetitions.
•	This paper demonstrates that during generation, LLMs undergo a phase shift from correct answers to hallucinations and then repetitions.
•	This paper indicates that methods to reduce text degeneration, like sampling, increase errors and hallucinations.
summary: This paper mainly analyzes the fallback behaviors of large language models (LLMs), which include sequence repetitions, degenerate text, and hallucinations. This paper conduct various experiments to explore the fallback behaviors in models from the same family that differ by  the amount of pretraining tokens, parameter count, or the inclusion of instruction-following training. It further discuss factors influencing the fallback behaviors of a LLM, such as sampling methods, simplifying the question and prompt engineering. The experiments reveal the following phenomenon:
1. Increasing model strength through extra pretraining, more parameters, or instruction tuning shifts fallback behaviors from simple to complex, i.e., from repetitions to hallucinations.
2. While LLMs have some internal capability to avoid hallucinations, this fallback behavior is inherent to their generation scheme and is likely unavoidable with current decoding methods. 
3. As models generate longer texts, they shift in their fallback behavior, first generating hallucinations and eventually producing degenerate text.
weaknesses: •	This paper seems to overclaim its contribution and conclusion. According to the datasets this paper used, the LLMs need to recall multiple facts that are related to the same topic. The other QA circumstances are ignored, including the simple QA like Natural Questions, multi-hop question answering like HotpotQA, open-ended long-formed question answering but beyond biography like ELI5. Also many other knowledge intensive tasks are also not discussed, which is also related with the epistemic uncertainty.
•	According to the paper, the fallback behaviors include sequence repetitions, degenerate text, and hallucinations. But degeneration is not discussed in the metric and experiment results.
•	The experiment setting of FQAMP seems not reasonable. If forcing uncertainty is essential, it is more natural to replace the subjects with a low popularity names. If replacing with made-up names and requiring LLMs to abstain, it is an OOD problem but not an uncertainty problem.

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: Below are some questions and comments:

- Footnote 2 is a bit hard to understand without having read later parts of the paper

- it's not immediately clear to me why Datasets 1-3 have different levels of epistemic uncertainty, could you clarify this?

References

[1] Hüllermeier, Eyke, and Willem Waegeman. "Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods." Machine learning 110.3 (2021): 457-506.

[2] Baan, Joris, et al. "Uncertainty in natural language generation: From theory to applications." arXiv preprint arXiv:2307.15703 (2023).
soundness: 3
strengths: - The idea of "epistemic uncertainty by construction" is an interesting way of circumventing problems with quantification of uncertainty.

- Overall, the paper seems methodologically sound.

- The paper is very well-written and easy to follow.

- The ideas presented in the paper can help guide further works on combating (unwanted) fallback behaviors of LLMs
summary: The paper studies different fallback behaviors of LLMs that occur under uncertainty,
For this, the authors construct settings where the model is expected to be uncertain.
They evaluate various models differing in both size and how long they are trained and compare greedy vs. sampling-based decoding methods.
The authors find that "stronger" models (trained longer, more parameters) exhibit more complex fallback behaviors which means that more hallucinations are produced.
weaknesses: - Epistemic uncertainty as a whole is a quite rich concept that might warrant a larger discussion. In particular, the authors focus mainly on uncertainty about facts but mostly neglect other forms / sources of uncertainty that are also often seen as contributing to epistemic uncertainty. An example is L417-419: verbatim recollection is hypothesized to be more present in OOD settings than factual uncertainty but OOD settings are often also thought to give rise to epistemic uncertainty, because not enough training data is observed in the domain, leading to a lack of knowledge. For example, the authors could discuss the relation of epistemic uncertainty and aleatoric uncertainty [1] and the sources of epistemic uncertainty further [2] 

- The main dataset is comparably small with only 95 items but also highly curated. Similarly, the other datasets are also fairly small, usually around 100 examples

- Some statements are a bit imprecise, for example: what is meant with internal capability to avoid hallucinations (L303) and what backs it up experimentally?

Meta-review: The paper investigates fallback behaviours in large language models (LLMs) under epistemic uncertainty, categorizing them into sequence repetitions, degenerate text, and hallucinations, and proposes a "fallback hierarchy" that connects these behaviours. Experiments are performed across various model configurations, including variations in pretraining data, parameter sizes, and decoding methods, demonstrating consistent patterns in how LLMs exhibit fallback behaviours as uncertainty increases. 

Despite the relevance of the topic and the thoroughness of some experiments, the paper has several shortcomings. While the authors provide an interesting characterization of fallback behaviours, the claims about their inevitability and possible mitigation remain insufficiently supported. The experimental setups rely heavily on small, somewhat contrived datasets that limit generalizability, and key findings are weakened by inconsistent terminology, a lack of clarity in experimental descriptions, and an overreliance on appendices for important results. Although the authors conducted additional experiments and added discussions on related uncertainties during the rebuttal period, these revisions did not adequately address concerns about the paper’s scope, generality, and practical implications. Given the limited generality of the findings, the unresolved methodological and presentation issues, and the overstated contributions, I recommend rejecting the paper.

Decision: Reject